Yuvraj Singh

Yuvraj Singh

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Yuvraj is an exceptional technical content writer with a strong computer science background. He has a talent for simplifying complex topics and making them accessible to readers. With a Bachelor's degree in Computer Science, Yuvraj has built a solid technical foundation, including programming, algorithms, and software development skills. This expertise forms the backbone of his writing career.

As a regular contributor to the Appy Pie blog, he has established himself as an expert in various fields, including app development, research, web design, and digital marketing. Yuvraj's writing style showcases both creativity and versatility. He is skilled at creating in-depth tutorials, thought-provoking opinions, and entertaining listicles that engage and inform his audience.

Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model

Yuvraj Singh
By Yuvraj Singh | May 29, 2024

Author(s): Kuan-Chih Huang, Xiangtai Li, Lu Qi, Shuicheng Yan, Ming-Hsuan Yang The paper introduces the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), a new benchmark designed to rigorously evaluate the performance of Video Large Multi-modal Models (Video-LMMs) across various real-world video contexts. Recent advancements have enabled these models to support diverse applications, including robotics, AI assistants, medical imaging, and autonomous vehicles, making [...]

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GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction

Yuvraj Singh
By Yuvraj Singh | May 29, 2024

Author(s): Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, Jiwen Lu 3D semantic occupancy prediction is a critical task for enhancing the robustness of vision-centric autonomous driving systems. This task involves obtaining the fine-grained 3D geometry and semantics of the surrounding scene. Traditional methods typically use dense grids, such as voxels, to represent scenes. However, these methods often overlook the sparsity of occupancy and the varying scales of objects, leadin[...]

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Matryoshka Multimodal Models

Yuvraj Singh
By Yuvraj Singh | May 29, 2024

Author(s): Mu Cai, Jianwei Yang, Jianfeng Gao, Yong Jae Lee Large multimodal models (LMMs) have demonstrated remarkable performance in visual-linguistic reasoning tasks. These models embed images into a fixed, large number of visual tokens, which are then fed into a Large Language Model (LLM). However, this approach becomes inefficient when dealing with dense visual scenarios like high-resolution images and videos, as it leads to an excessive number of tokens. While token pruning a[...]

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InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation

Yuvraj Singh
By Yuvraj Singh | May 27, 2024

Author(s): Yuchi Wang, Junliang Guo, Jianhong Bai, Runyi Yu, Tianyu He, Xu Tan, Xu Sun, Jiang Bian Diffusion models have emerged as a powerful tool for text-to-image generation, producing high-quality and diverse images from textual descriptions. However, the generated images often lack semantic consistency and fail to accurately capture the intended meaning of the input text. This paper introduces a novel approach to address this limitation by incorporating semantic guidance into the[...]

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Looking Backward: Streaming Video-to-Video Translation with Feature Banks

Yuvraj Singh
By Yuvraj Singh | May 27, 2024

Author(s): Feng Liang, Akio Kodaira, Chenfeng Xu, Masayoshi Tomizuka, Kurt Keutzer, Diana Marculescu This paper presents a novel approach to improving the efficiency and quality of image generation using diffusion models. The proposed method introduces an efficient sampling technique that significantly reduces the computational cost while maintaining high-quality image outputs. Diffusion models have shown great promise in generating high-fidelity images, but their practical applicatio[...]

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ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

Yuvraj Singh
By Yuvraj Singh | May 27, 2024

Author(s): Chunjiang Ge, Sijie Cheng, Ziming Wang, Jiale Yuan, Yuan Gao, Jun Song, Shiji Song, Gao Huang, Bo Zheng This paper presents a novel approach to image classification by employing sparse neural networks, aiming to enhance both efficiency and robustness. Sparse neural networks are designed to reduce the number of active parameters, thereby decreasing computational complexity and memory usage without significantly compromising performance. The proposed method introduces a struc[...]

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4D Panoptic Scene Graph Generation

Yuvraj Singh
By Yuvraj Singh | May 23, 2024

Author(s): Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan, Muzammal Naseer, Federico Tombari, Fahad Shahbaz Khan, Salman Khan "4D Panoptic Scene Graph Generation" introduces a novel representation called the 4D Panoptic Scene Graph (PSG-4D) to enhance artificial intelligence's understanding of dynamic 4D environments. This representation bridges the gap between raw visual data perceived in a 4D world and high-level visual understanding. PSG-4D abstracts rich 4D sensory d[...]

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Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation

Yuvraj Singh
By Yuvraj Singh | May 23, 2024

Author(s): Andres Hernandez, Zhongqi Miao, Luisa Vargas, Rahul Dodhia, Juan Lavista The paper titled "Pytorch-Wildlife: An Open-Source Deep Learning Platform for Wildlife Monitoring" addresses the urgent need for large-scale wildlife monitoring in response to the alarming decline in global biodiversity. This decline is driven by various factors, necessitating the development of automated deep learning methods for data processing in wildlife monitoring. However, the application of th[...]

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Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal Slices

Yuvraj Singh
By Yuvraj Singh | May 23, 2024

Author(s): Nathaniel Cohen, Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli The paper titled "Slicedit: Text-based Video Editing Using Pretrained Text-to-Image Diffusion Models" addresses the challenge of leveraging pretrained text-to-image (T2I) diffusion models for video editing. T2I diffusion models are known for their state-of-the-art performance in image synthesis and editing. However, applying these models to video editing has been difficult due to th[...]

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BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once

Yuvraj Singh
By Yuvraj Singh | May 22, 2024

Author(s): Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Brian Piening, Carlo Bifulco, Mu Wei, Hoifung Poon, Sheng Wang In the rapidly evolving field of machine learning, the development of models that are both interpretable and efficient represents a critical challenge. This paper introduces a novel structured sparse learning framework designed to address this challenge by improving model interpretability without compro[...]

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Personalized Residuals and Localized Attention-Guided Sampling for Efficient Concept-Driven Generation in Text-to-Image Diffusion Models

Yuvraj Singh
By Yuvraj Singh | May 22, 2024

Author(s): Cusuh Ham, Matthew Fisher, James Hays, Nicholas Kolkin, Yuchen Liu, Richard Zhang, Tobias Hinz This paper introduces a novel approach for efficient concept-driven generation using text-to-image diffusion models, combining personalized residuals and localized attention-guided sampling. The method begins by representing concepts through the freezing of weights in a pretrained text-conditioned diffusion model and the learning of low-rank residuals for a select subset of the mo[...]

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OmniGlue: A Generalizable Learnable Image Matcher Guided by Vision Foundation Models

Yuvraj Singh
By Yuvraj Singh | May 22, 2024

Author(s): Hanwen Jiang, Arjun Karpur, Bingyi Cao, Qixing Huang, Andre Araujo The field of image matching has seen a rapid development of learnable feature matching techniques, consistently pushing the boundaries of performance on standard benchmarks. However, a closer examination reveals that despite these advancements, their applicability to real-world scenarios is hindered by limited generalization abilities when faced with novel image domains. OmniGlue, introduced in this paper,[...]

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Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo

Yuvraj Singh
By Yuvraj Singh | May 21, 2024

Author(s): Tianqi Liu, Guangcong Wang, Shoukang Hu, Liao Shen, Xinyi Ye, Yuhang Zang, Zhiguo Cao, Wei Li, Ziwei Liu The paper introduces MVSGaussian, a novel approach for 3D Gaussian representation derived from Multi-View Stereo (MVS) that efficiently reconstructs unseen scenes. This method is designed to enhance the performance of 3D scene reconstruction and view synthesis through several key innovations. Firstly, MVSGaussian utilizes MVS to encode geometry-aware Gaussian represen[...]

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Locational marginal burden: Quantifying the equity of optimal power flow solutions

Yuvraj Singh
By Yuvraj Singh | May 21, 2024

Author(s): Samuel Talkington, Amanda West, Rabab Haider The paper addresses the challenge of ensuring fair distribution of benefits in electric power systems, a significant issue in energy policymaking. Traditional power system engineering studies struggle to quantify these efforts effectively. To bridge this gap, the authors introduce the concept of locational marginal burden (LMB). LMB serves as an interface between energy pricing equity, measured by energy burden, and the optimal p[...]

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Images that Sound: Composing Images and Sounds on a Single Canvas

Yuvraj Singh
By Yuvraj Singh | May 21, 2024

Author(s): Ziyang Chen, Daniel Geng, Andrew Owens Spectrograms, which are 2D representations of sound, differ significantly from the images found in the visual world. When natural images are played as spectrograms, they produce unnatural sounds. However, this paper demonstrates the possibility of synthesizing spectrograms that simultaneously resemble natural images and sound like natural audio, referred to as "images that sound." The approach presented in this study is simple and zer[...]

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Reconstruction of Manipulated Garment with Guided Deformation Prior

Yuvraj Singh
By Yuvraj Singh | May 20, 2024

Author(s): Ren Li, Corentin Dumery, Zhantao Deng, Pascal Fua Modeling the shape of garments has garnered significant attention, but most existing approaches assume the garments are worn, limiting the range of shapes they can take. This paper addresses the challenge of shape recovery when garments are manipulated rather than worn, resulting in a broader range of possible shapes. The study introduces an extension to the implicit sewing patterns (ISP) model by incorporating a diffusion-b[...]

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A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers

Yuvraj Singh
By Yuvraj Singh | May 20, 2024

Author(s): Kaiyu Huang, Fengran Mo, Hongliang Li, You Li, Yuanchi Zhang, Weijian Yi, Yulong Mao, Jinchen Liu, Yuzhuang Xu, Jinan Xu, Jian-Yun Nie, Yang Liu The rapid advancement of large language models (LLMs) has showcased significant multilingual capabilities in natural language processing, garnering widespread attention from both academia and industry. To address potential discrimination and improve usability and accessibility for diverse language user groups, the development of [...]

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Observational Scaling Laws and the Predictability of Language Model Performance

Yuvraj Singh
By Yuvraj Singh | May 20, 2024

Author(s): Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto Understanding the variation in language model performance with scale is essential for benchmarking and algorithm development. Traditional scaling laws, which require training models at various scales, have been limited in their application due to the extensive resources needed. This paper introduces an alternative, observational approach that constructs scaling laws from approximately 80 publicly available models, elimina[...]

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Analogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model

Yuvraj Singh
By Yuvraj Singh | May 17, 2024

Author(s): Zheng Gu, Shiyuan Yang, Jing Liao, Jing Huo, Yang Gao The paper titled "Analogist: A Novel Inference-Based Visual In-Context Learning Approach" explores advancements in Visual In-Context Learning (ICL), a field that leverages analogical reasoning to perform various tasks with limited example pairs. Traditional training-based visual ICL methods face challenges in generalizing to unseen tasks and necessitate the collection of diverse task datasets. Conversely, existing infere[...]

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Text-to-Vector Generation with Neural Path Representation

Yuvraj Singh
By Yuvraj Singh | May 17, 2024

Author(s): Peiying Zhang, Nanxuan Zhao, Jing Liao The paper titled "Neural Path Representation for Text-to-Vector Generation" addresses the challenges associated with creating and editing vector graphics, a task that traditionally demands significant creativity and design expertise. Vector graphics are highly valued in digital art for their scalability and layer-wise properties, but the process of generating these graphics can be time-consuming. Recent advancements in text-to-vector ([...]

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Toon3D: Seeing Cartoons from a New Perspective

Yuvraj Singh
By Yuvraj Singh | May 17, 2024

Author(s): Ethan Weber, Riley Peterlinz, Rohan Mathur, Frederik Warburg, Alexei A. Efros, Angjoo Kanazawa In the recent study detailed in "Recovering 3D Structure from Cartoon and Anime Drawings," researchers tackle the challenge of interpreting and reconstructing the three-dimensional structure of scenes depicted in hand-drawn images, specifically focusing on cartoons and anime. This innovative work addresses the inherent inconsistency in these artistic creations, where scenes and ob[...]

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Classifying geospatial objects from multiview aerial imagery using semantic meshes

Yuvraj Singh
By Yuvraj Singh | May 16, 2024

Author(s): David Russell, Ben Weinstein, David Wettergreen, Derek Young The paper introduces a novel approach to utilizing aerial imagery for Earth science and natural resource management, specifically targeting the limitations of traditional methods that rely on synthesized top-down "orthomosaic" images. These conventional methods often lack vertical information and may include processing artifacts, which can hinder accurate predictions, such as tree species classification. The pr[...]

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Intrinsic Voltage Offsets in Memcapacitive Bio-Membranes Enable High-Performance Physical Reservoir Computing

Yuvraj Singh
By Yuvraj Singh | May 16, 2024

Author(s): Ahmed S. Mohamed, Anurag Dhungel, Md Sakib Hasan, Joseph S. Najem The paper presents a novel approach to reservoir computing, a brain-inspired machine learning framework designed for processing temporal data by mapping inputs into high-dimensional spaces. Traditional physical reservoir computers (PRCs) often rely on homogeneous device arrays, which use input encoding methods and large stochastic device-to-device variations to achieve nonlinearity and high-dimensional mappi[...]

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BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation

Yuvraj Singh
By Yuvraj Singh | May 16, 2024

Author(s): Yunhao Ge, Yihe Tang, Jiashu Xu, Cem Gokmen, Chengshu Li, Wensi Ai, Benjamin Jose Martinez, Arman Aydin, Mona Anvari, Ayush K Chakravarthy, Hong-Xing Yu, Josiah Wong, Sanjana Srivastava, Sharon Lee, Shengxin Zha, Laurent Itti, Yunzhu Li, Roberto Martín-Martín, Miao Liu, Pengchuan Zhang, Ruohan Zhang, Li Fei-Fei, Jiajun Wu The paper introduces the BEHAVIOR Vision Suite (BVS), a comprehensive set of tools and assets designed to generate fully customized synthetic data for t[...]

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SciFIBench: Benchmarking Large Multimodal Models for Scientific Figure Interpretation

Yuvraj Singh
By Yuvraj Singh | May 15, 2024

Author(s): Jonathan Roberts, Kai Han, Neil Houlsby, Samuel Albanie The paper introduces SciFIBench, a benchmark designed to evaluate the capabilities of large multimodal models (LMMs) in the domain of scientific research, specifically focusing on the interpretation of scientific figures. LMMs have demonstrated flexibility and generalizability across various tasks and fields, yet their potential in aiding scientific research remains underexplored. Understanding and interpreting figures i[...]

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CinePile: A Long Video Question Answering Dataset and Benchmark

Yuvraj Singh
By Yuvraj Singh | May 15, 2024

Author(s): Ruchit Rawal, Khalid Saifullah, Ronen Basri, David Jacobs, Gowthami Somepalli, Tom Goldstein The paper introduces CinePile, a novel dataset and benchmark specifically designed to address the limitations of current datasets in long-form video understanding. Traditional datasets often fail to provide genuine long-form comprehension challenges, as many tasks can be effectively tackled by analyzing just a few random frames from a video. CinePile aims to overcome this issue by o[...]

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Efficient Vision-Language Pre-training by Cluster Masking

Yuvraj Singh
By Yuvraj Singh | May 15, 2024

Author(s): Zihao Wei, Zixuan Pan, Andrew Owens The paper presents a novel strategy for enhancing visual-language contrastive learning by introducing a masking technique that targets clusters of visually similar image patches. This method, distinct from traditional approaches, leverages the raw pixel intensities of image patches to determine their visual similarity and subsequently masks them during training iterations. The primary advantage of this technique is that it compels the model[...]

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Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots

Yuvraj Singh
By Yuvraj Singh | May 14, 2024

Author(s): Chengyue Wu, Yixiao Ge, Qiushan Guo, Jiahao Wang, Zhixuan Liang, Zeyu Lu, Ying Shan, Ping Luo The advancement of Multi-modal Large Language Models (MLLMs) has garnered significant attention due to their enhanced performance in visual contexts. However, their ability to convert visual figures into executable code has not been thoroughly evaluated. To address this gap, the study introduces Plot2Code, a comprehensive visual coding benchmark designed for an in-depth assessment o[...]

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MambaOut: Do We Really Need Mamba for Vision?

Yuvraj Singh
By Yuvraj Singh | May 14, 2024

Author(s): Weihao Yu, Xinchao Wang Mamba, an architecture featuring an RNN-like token mixer based on the state space model (SSM), was introduced to address the quadratic complexity of the attention mechanism and applied to vision tasks. However, Mamba's performance in vision tasks often falls short compared to convolutional and attention-based models. This paper explores the fundamental nature of Mamba, concluding that it is best suited for tasks with long-sequence and autoregressi[...]

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SPIN: Simultaneous Perception, Interaction and Navigation

Yuvraj Singh
By Yuvraj Singh | May 14, 2024

Author(s): Shagun Uppal, Ananye Agarwal, Haoyu Xiong, Kenneth Shaw, Deepak Pathak This paper addresses the enduring challenge of mobile manipulation, which remains complex despite advancements in manipulation and locomotion. Mobile manipulation systems must perform a variety of long-term tasks in unstructured and dynamic environments, presenting challenges such as coordinating the base and arm, relying on onboard perception, and integrating all components simultaneously. Traditional[...]

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Value Augmented Sampling for Language Model Alignment and Personalization

Yuvraj Singh
By Yuvraj Singh | May 13, 2024

Author(s): Seungwook Han, Idan Shenfeld, Akash Srivastava, Yoon Kim, Pulkit Agrawal The adaptation of large language models (LLMs) to cater to diverse human preferences, learn new skills, and unlearn harmful behavior is a crucial challenge. Traditional search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are effective but impractical due to their high inference cost. On the other hand, reinforcement learning (RL) methods are computationally efficient but struggle with o[...]

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Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data

Yuvraj Singh
By Yuvraj Singh | May 13, 2024

Author(s): Yonghao Xu, Pedram Ghamisi, Yannis Avrithis The problem of multi-target unsupervised domain adaptation (UDA) in semantic segmentation aims to develop a unified model capable of addressing the domain shift between multiple target domains. This challenge has been recently introduced in cross-domain semantic segmentation due to the difficulty of obtaining annotations for dense predictions. Existing solutions typically require labeled data from the source domain and unlabeled [...]

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Conformal Validity Guarantees Exist for Any Data Distribution

Yuvraj Singh
By Yuvraj Singh | May 13, 2024

Author(s): Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria The growing adoption of machine learning (ML) has led to a pressing need for practitioners to quantify and control the risks associated with these systems. This challenge is particularly significant when ML systems have autonomy to collect their own data, such as in black-box optimization and active learning, where their actions induce sequential feedback-loop shifts in the data distribution. Conformal prediction has eme[...]

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OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies

Yuvraj Singh
By Yuvraj Singh | May 9, 2024

Author(s): Lingdong Kong, Youquan Liu, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi The paper presents OpenESS, a novel approach to event-based semantic segmentation (ESS), a fundamental yet challenging task in event camera sensing. The scalability of ESS is often limited by the difficulties in interpreting and annotating event data. While domain adaptation from images to event data can alleviate this issue, data representational differences pose additional challenges that need to be[...]

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Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving

Yuvraj Singh
By Yuvraj Singh | May 9, 2024

Author(s): Lingdong Kong, Xiang Xu, Jiawei Ren, Wenwei Zhang, Liang Pan, Kai Chen, Wei Tsang Ooi, Ziwei Liu The paper presents LaserMix++, an advanced framework designed to enhance the efficiency of data utilization in 3D scene understanding for autonomous driving. This is achieved by extending semi-supervised learning for LiDAR semantic segmentation, which leverages the inherent spatial priors of driving scenes and multi-sensor complements to increase the effectiveness of unlabeled d[...]

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You Only Cache Once: Decoder-Decoder Architectures for Language Models

Yuvraj Singh
By Yuvraj Singh | May 9, 2024

Author(s): Yutao Sun, Li Dong, Yi Zhu, Shaohan Huang, Wenhui Wang, Shuming Ma, Quanlu Zhang, Jianyong Wang, Furu Wei The paper introduces a novel architecture, YOCO, designed for large language models. This architecture is unique The paper introduces a novel architecture, YOCO, designed for large language models. This architecture is unique in its approach as it only caches key-value pairs once. YOCO is composed of two main components: a self-decoder and a cross-decoder. The self-deco[...]

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QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Yuvraj Singh
By Yuvraj Singh | May 8, 2024

Author(s): Yujun Lin, Haotian Tang, Shang Yang, Zhekai Zhang, Guangxuan Xiao, Chuang Gan, Song Han The acceleration of large language model (LLM) inference is achievable through quantization. The research community is currently exploring lower precision than INT8, such as INT4. However, the state-of-the-art INT4 quantization techniques only speed up low-batch, edge LLM inference and fail to deliver performance gains in large-batch, cloud-based LLM serving. A significant runtime overhea[...]

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ChatHuman: A Language-Driven Human Understanding System

Yuvraj Singh
By Yuvraj Singh | May 8, 2024

Author(s): Jing Lin, Yao Feng, Weiyang Liu, Michael J. Black The paper titled “ChatHuman: A Language-Driven Human Understanding System” discusses the development of a unique system that integrates various methods to detect, estimate, and analyze properties of people in images. These properties include the estimation of 3D pose, shape, contact, human-object interaction, emotion, and more. However, these methods often work in isolation rather than synergistically. To address thi[...]

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Tactile-Augmented Radiance Fields

Yuvraj Singh
By Yuvraj Singh | May 8, 2024

Author(s): Yiming Dou, Fengyu Yang, Yi Liu, Antonio Loquercio, Andrew Owens The paper titled “Tactile-Augmented Radiance Field (TaRF): A Scene Representation” introduces a unique scene representation, known as a tactile-augmented radiance field (TaRF), that unifies vision and touch within a shared 3D space. This representation can be utilized to estimate the visual and tactile signals for a specific 3D position within a scene. The TaRF of a scene is captured from a collection of [...]

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Language-Image Models with 3D Understanding

Yuvraj Singh
By Yuvraj Singh | May 7, 2024

Author(s): Jang Hyun Cho, Boris Ivanovic, Yulong Cao, Edward Schmerling, Yue Wang, Xinshuo Weng, Boyi Li, Yurong You, Philipp Krähenbühl, Yan Wang, Marco Pavone This paper presents Cube-LLM, a novel Multi-modal Large Language Model (MLLM) that extends the perceptual capabilities of MLLMs to understand and reason about images in three-dimensional space. Unlike traditional models that primarily focus on 2D vision and language tasks, Cube-LLM leverages a large-scale pre-training dataset [...]

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An Empty Room is All We Want: Automatic Defurnishing of Indoor Panoramas

Yuvraj Singh
By Yuvraj Singh | May 7, 2024

Author(s): Mira Slavcheva, Dave Gausebeck, Kevin Chen, David Buchhofer, Azwad Sabik, Chen Ma, Sachal Dhillon, Olaf Brandt, Alan Dolhasz This paper introduces a novel pipeline designed to enhance inpainting outcomes in the specific task of defurnishing, which involves the removal of furniture items from indoor panorama images. The proposed method capitalizes on Stable Diffusion, a technique that significantly improves the quality of inpainting by incorporating increased context, domain[...]

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Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs

Yuvraj Singh
By Yuvraj Singh | May 7, 2024

Author(s): Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan, Muzammal Naseer, Federico Tombari, Fahad Shahbaz Khan, Salman Khan The paper introduces the Complex Video Reasoning and Robustness Evaluation Suite (CVRR-ES), a new benchmark designed to rigorously evaluate the performance of Video Large Multi-modal Models (Video-LMMs) across various real-world video contexts. Recent advancements have enabled these models to support diverse applications, including robotics, AI as[...]

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What matters when building vision-language models?

Yuvraj Singh
By Yuvraj Singh | May 6, 2024

Author(s) : Hugo Laurençon, Léo Tronchon, Matthieu Cord, Victor Sanh The paper titled “Idefics2: An Efficient Foundational Vision-Language Model” discusses the burgeoning interest in vision-language models (VLMs), propelled by advancements in large language models and vision transformers. Despite the wealth of research in this area, the paper notes that crucial decisions in VLM design often lack justification. This lack of substantiation hinders progress in the field by obscuri[...]

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On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?

Yuvraj Singh
By Yuvraj Singh | May 6, 2024

Author(s) : Maxime Zanella, Ismail Ben Ayed The paper titled “MeanShift for Test-time Augmentation (MTA)” presents a robust method that outperforms prompt-based techniques without the need for intensive training. This method is ideal for both standalone and API-based applications. Unlike previous test-time augmentation techniques that rely on ad hoc rules, such as a confidence threshold, to filter the augmented views, MTA incorporates a quality assessment variable for each view d[...]

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DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos

Yuvraj Singh
By Yuvraj Singh | May 6, 2024

Author(s) : Wen-Hsuan Chu, Lei Ke, Katerina Fragkiadaki The paper titled “DreamScene4D” introduces a novel approach to generate three-dimensional dynamic scenes of multiple objects from monocular in-the-wild videos. This is achieved by leveraging existing Video Language Models (VLMs) that can track 2D video objects and current generative models that provide powerful visual priors for synthesizing novel views for the highly under-constrained 2D-to-3D object lifting. The key ins[...]

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Training-Free Consistent Text-to-Image Generation

Yuvraj Singh
By Yuvraj Singh | May 2, 2024

Author(s) : Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior Wolf, Gal Chechik, Yuval Atzmon ConsiStory is a groundbreaking training-free approach that addresses the challenge of consistently portraying the same subject across diverse prompts in text-to-image models. While these models offer unprecedented creative flexibility by allowing users to guide the image generation process through natural language, maintaining subject consistency has been a significant hurdle. Exist[...]

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No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO

Yuvraj Singh
By Yuvraj Singh | May 2, 2024

Author(s) : Skander Moalla, Andrea Miele, Razvan Pascanu, Caglar Gulcehre Proximal Policy Optimization (PPO), a popular on-policy reinforcement learning (RL) method, is not immune to the challenges posed by non-stationarity in RL environments. Despite the common belief that on-policy methods can train indefinitely, this study reveals that PPO agents are also susceptible to feature rank deterioration and loss of plasticity, which can lead to a collapse in performance. The autho[...]

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Spectrally Pruned Gaussian Fields with Neural Compensation

Yuvraj Singh
By Yuvraj Singh | May 2, 2024

Author(s) : Runyi Yang, Zhenxin Zhu, Zhou Jiang, Baijun Ye, Xiaoxue Chen, Yifei Zhang, Yuantao Chen, Jian Zhao, Hao Zhao SUNDAE, a memory-efficient Gaussian field, addresses the high memory consumption issue associated with 3D Gaussian Splatting, a novel 3D representation known for its fast rendering speed and high rendering quality. The high memory footprint of well-trained Gaussian fields, which can utilize millions of Gaussian primitives and hundreds of megabytes of memory, is att[...]

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CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models

Yuvraj Singh
By Yuvraj Singh | May 2, 2024

Author(s) : Qinghe Wang, Baolu Li, Xiaomin Li, Bing Cao, Liqian Ma, Huchuan Lu, Xu Jia CharacterFactory is a groundbreaking framework that enables the sampling of new characters with consistent identities in the latent space of Generative Adversarial Networks (GANs) for diffusion models. This innovative approach addresses the limitations of current text-to-image models, which cannot directly generate images with consistent, newly coined identities. The framework considers the word[...]

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ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving

Yuvraj Singh
By Yuvraj Singh | May 1, 2024

Author(s): Jiehui Huang, Xiao Dong, Wenhui Song, Hanhui Li, Jun Zhou, Yuhao Cheng, Shutao Liao, Long Chen, Yiqiang Yan, Shengcai Liao, Xiaodan Liang ConsistentID is a groundbreaking method designed for diverse identity-preserving portrait generation using fine-grained multimodal facial prompts and a single reference image. This innovative approach addresses the limitations of existing diffusion-based technologies, which struggle to achieve high-fidelity and detailed identity consis[...]

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PuLID: Pure and Lightning ID Customization via Contrastive Alignment

Yuvraj Singh
By Yuvraj Singh | May 1, 2024

Author(s) : Zinan Guo, Yanze Wu, Zhuowei Chen, Lang Chen, Qian He The paper introduces Pure and Lightning ID customization (PuLID), an innovative tuning-free method for customizing identities in text-to-image generation models. PuLID combines a Lightning T2I branch with a standard diffusion branch, enabling the incorporation of both contrastive alignment loss and accurate ID loss. This approach minimizes disruption to the original model while ensuring high fidelity in the generated i[...]

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Hallucination of Multimodal Large Language Models: A Survey

Yuvraj Singh
By Yuvraj Singh | April 30, 2024

Author(s) : Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, Mike Zheng Shou Multimodal Large Language Models (MLLMs), also known as Large Vision-Language Models (LVLMs), have shown significant advancements and remarkable capabilities in multimodal tasks. Despite these promising developments, MLLMs often produce outputs that are inconsistent with the visual content. This inconsistency, known as hallucination, poses considerable challenges to their practical de[...]

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Stylus: Automatic Adapter Selection for Diffusion Models

Yuvraj Singh
By Yuvraj Singh | April 30, 2024

Author(s) : Michael Luo, Justin Wong, Brandon Trabucco, Yanping Huang, Joseph E. Gonzalez, Zhifeng Chen, Ruslan Salakhutdinov, Ion Stoica When it comes to making high-resolution, customized images, fine-tuned adapters have become a cheaper option for scaling the base models with more data or parameters. The open-source community has learned how to use adapters, and this has led to the creation of a large database with over 100,000 adapters, many of which are highly customized without[...]

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DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editing

Yuvraj Singh
By Yuvraj Singh | April 30, 2024

Author(s) : Minghao Chen, Iro Laina, Andrea Vedaldi The task of editing 3D objects and scenes based on open-ended language instructions presents a unique set of challenges. The conventional approach to address this problem involves using a 2D image generator or editor to guide the 3D editing process. However, this method often proves to be time-consuming due to the need to update computationally intensive 3D representations such as a neural radiance field. Moreover, it relies on pote[...]

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Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos

Yuvraj Singh
By Yuvraj Singh | April 29, 2024

Author(s) : Zhengze Xu, Mengting Chen, Zhao Wang, Linyu Xing, Zhonghua Zhai, Nong Sang, Jinsong Lan, Shuai Xiao, Changxin Gao This paper tackles the challenge of video try-on, an area where previous research has yielded limited success. The core difficulty lies in simultaneously preserving intricate clothing details and generating realistic, coherent motions throughout the video. To address these challenges, the authors propose "Tunnel Try-on," a novel diffusion-based framework. T[...]

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MaPa: Text-driven Photorealistic Material Painting for 3D Shapes

Yuvraj Singh
By Yuvraj Singh | April 29, 2024

Author(s) : Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, Xiaowei Zhou The generation of materials for 3D meshes from text descriptions is an innovative approach presented in this research paper. Unlike traditional methods that focus on texture map synthesis, the proposed method introduces the generation of segment-wise procedural material graphs, offering high-quality rendering and substantial flexibility in editing. The k[...]

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Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models

Yuvraj Singh
By Yuvraj Singh | April 29, 2024

Author(s) : Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng, Xu Yang This paper investigates the ability of Large Vision-Language Models (LVLMs) to generate detailed and accurate descriptions of visual content. While LVLMs have become increasingly sophisticated in their ability to process and integrate visual and textual data, a less explored area is their potential to create fine-grained descriptions. This research addresses this gap in knowledge by examining how effectively LVLM[...]

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TalkingGaussian: Structure-Persistent 3D Talking Head Synthesis via Gaussian Splatting

Yuvraj Singh
By Yuvraj Singh | April 25, 2024

Author(s) : Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, Lin Gu Radiance fields have demonstrated impressive capabilities in synthesizing lifelike 3D talking heads. However, the prevailing paradigm, which presents facial motions by directly modifying point appearance, may lead to distortions in dynamic regions due to the difficulty in fitting steep appearance changes. To address this challenge, the researchers introduce Talking Gaussian, a deformation-based rad[...]

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From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation

Yuvraj Singh
By Yuvraj Singh | April 25, 2024

Author(s) : Zehuan Huang, Hongxing Fan, Lipeng Wang, Lu Sheng Recent advancements in controllable human image generation have enabled zero-shot generation using structural signals, such as pose or depth information, or facial appearance. However, generating human images conditioned on multiple parts of human appearance remains a significant challenge in the field. To address this challenge, the researchers introduce Parts to Whole, a novel framework designed for generating customi[...]

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UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition

Yuvraj Singh
By Yuvraj Singh | April 25, 2024

Author(s) : Bin Wang, Zhuangcheng Gu, Chao Xu, Bo Zhang, Botian Shi, Conghui He This paper introduces UniMER, a groundbreaking dataset that provides the first comprehensive study on Mathematical Expression Recognition (MER) in complex real-world scenarios. The UniMER dataset consists of two distinct components: a large-scale training set, UniMER-1M, and a meticulously designed test set, UniMER-Test. UniMER-1M offers an unprecedented scale and diversity, comprising one million trai[...]

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