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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是421-430 订阅

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Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of t...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Hongjie Dedhia, Bhishma Jha, Niraj K. Princeton Univ Princeton NJ 08540 USA

ISBN: (纸本)9798350353006

Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However, most token pruning methods require computationally expensive fine-tuning, which is undesirable in many edge deployment cases. In this work, we propose Zero-TPrune, the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead, Zero-TPrune can prune large models at negligible computational cost, switch between different pruning configurations at no computational cost, and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning, Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state- of-the-art pruning methods that require fine-tuning, Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods, Zero-TPrune reduces accuracy loss by up to 49% with similar FLOPs budgets. Project webpage: https://***/zerotprune.

关键词： attention efficiency token pruning vision transformer zero-shot

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HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large vision-Language Models

HALLUSIONBENCH: An Advanced Diagnostic Suite for Entangled L...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Guan, Tianrui Liu, Fuxiao Wu, Xiyang Xian, Ruiqi Li, Zongxia Liu, Xiaoyu Wang, Xijun Chen, Lichang Huang, Furong Yacoob, Yaser Manocha, Dinesh Zhou, Tianyi Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350353006

We introduce "HALLUSIONBENCH(1)," a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(ision), Gemini Pro vision, Claude 3, and LLaVA-1.5, by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on HALLUSIONBENCH, we benchmarked 15 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only highlights the observed failure modes, including language hallucination and visual illusion but also deepens an understanding of these pitfalls. Our comprehensive case studies within HALLUSIONBENCH shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights, we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://***/tianyi-lab/HallusionBench.

关键词： Evaluation Dataset Hallucination vision language model VLM Evaluation

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Lookahead Exploration with Neural Radiance Representation for Continuous vision-Language Navigation

Lookahead Exploration with Neural Radiance Representation fo...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Zihan Li, Xiangyang Yang, Jiahao Liu, Yeqi Hu, Junjie Jiang, Ming Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA Indiana Univ Dept Human Ctr Comp Indianapolis IN 46204 USA

ISBN: (纸本)9798350353006

vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method. The code is available at https://***/MrZihan/HNR-VLN

关键词： Lookahead Exploration Neural Radiance Fields vision-and-Language Navigation

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3D Human Pose Perception from Egocentric Stereo Videos

3D Human Pose Perception from Egocentric Stereo Videos

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Akada, Hiroyasu Wang, Jian Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2, UnrealEgo-RW, and trained models are available on our project page(1) and Benchmark Challenge(2).

关键词： Egocentric 3D human pose estimation First-person view Stereo vision

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Towards Language-Driven Video Inpainting via Multimodal Large Language Models

Towards Language-Driven Video Inpainting via Multimodal Larg...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Jianzong Li, Xiangtai Si, Chenyang Zhou, Shangchen Yang, Jingkang Zhang, Jiangning Li, Yining Chen, Kai Tong, Yunhai Liu, Ziwei Loy, Chen Change Peking Univ Natl Key Lab Gen Artificial Intelligence Beijing Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Shanghai AI Lab Shanghai Peoples R China PKU Wuhan Inst Artificial Intelligence Wuhan Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

We introduce a new task - language-driven video inpainting, which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that de-pend on manually labeled binary masks, a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset, containing 5,650 videos and 9,091 inpainting results, to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework, the first end-to-end baseline for this task, integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets, code, and models publicly available at https://***/jianzongwu/Language-Driven-Video-Inpainting.

关键词： computer vision Multimodal Learning Text-to-Video Generation Video Inpainting

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The Neglected Tails in vision-Language Models

The Neglected Tails in Vision-Language Models

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Parashar, Shubham Lin, Zhiqiu Liu, Tian Dong, Xiangjue Li, Yanan Ramanan, Deva Caverlee, James Kong, Shu Texas A&M Univ College Stn TX 77840 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Zhejiang Lab Hangzhou Peoples R China Univ Macau Taipa Macao Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example, although CLIP achieves impressive accuracy on ImageNet (60-80%), its performance drops below 10% for more than ten concepts like night snake, presumably due to their limited presence in the pretraining data. However, measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets, such as LAION, exhibit a long-tailed concept distribution, yielding biased performance in VLMs. We also find that downstream applications of VLMs, including visual chatbots (e.g., GPT-4V) and text-to-image models (e.g., Stable Diffusion), often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs, we propose REtrieval-Augmented Learning (REAL). First, instead of prompting VLMs using the original class names, REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second, REAL trains a linear classifier on a small yet balanced set of pretraining data re-trieved using concept synonyms. REAL surpasses the previous zero-shot SOTA, using 400x less storage and 10,000x less training time!

关键词： Long tailed recognition vision-Language Models Zero-Shot recognition

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Dual Memory Networks: A Versatile Adaptation Approach for vision-Language Models

Dual Memory Networks: A Versatile Adaptation Approach for Vi...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Yabin Zhu, Wenjie Tang, Hui Ma, Zhiyuan Zhou, Kaiyang Zh, Lei HKPolyU Hong Kong Peoples R China OPPO Hong Kong Peoples R China HKUST Hong Kong Peoples R China HKBU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at https://***/YBZh/DMN.

关键词： dual memory networks versatile adaptation vision-language models

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VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding

VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wasim, Syed Talal Naseer, Muzammal Khan, Salman Yang, Ming-Hsuan Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and pre-defined vocabularies, our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content, achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably, the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore, in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions, our model surpasses the recent best-performing models by 4.88 m vIoU and 1.83% accuracy, demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

关键词： MultiModal Open Vocabulary Video Grounding vision Language

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AHIVE: Anatomy-aware Hierarchical vision Encoding for Interactive Radiology Report Retrieval

AHIVE: Anatomy-aware Hierarchical Vision Encoding for Intera...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yan, Sixing Cheung, William K. Tsang, Ivor W. Chiu, Keith Tong, Terence M. Cheung, Ka Chun Seel, Simon Hong Kong Baptist Univ Hong Kong Peoples R China Agcy Sci Technol & Res CFAR Singapore Singapore Agcy Sci Technol & Res IHPC Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Univ Technol Sydney AAII Sydney NSW Australia Queen Elizabeth Hosp Hong Kong Peoples R China Kwong Wah Hosp Hong Kong Peoples R China Tuen Mun Hosp Hong Kong Peoples R China NVIDIA Corp NVIDIA AI Technol Ctr Santa Clara CA USA

ISBN: (纸本)9798350353006

Automatic radiology report generation using deep learning models has been recently explored and found promising. Neural decoders are commonly used for the report generation, where irrelevant and unfaithful contents are unavoidable. The retrieval-based approach alleviates the limitation by identifying reports which are relevant to the input to assist the generation. To achieve clinically accurate report retrieval, we make reference to clinicians' diagnostic steps of examining a radiology image where anatomical and diagnostic details are typically focused, and propose a novel hierarchical visual concept representation called anatomy-aware hierarchical vision encoding (AHIVE). To learn AHIVE, we first derive a methodology to extract hierarchical diagnostic descriptions from radiology reports and develop a CLIP-based framework for the model training. Also, the hierarchical architecture of AHIVE is designed to support interactive report retrieval so that report revision made at one layer can be propagated to the subsequent ones to trigger other necessary revisions. We conduct extensive experiments and show that AHIVE can outperform the SOTA vision-language retrieval methods in terms of clinical accuracy by a large margin. We provide also a case study to illustrate how it enables interactive report retrieval.

关键词： image-to-text retrieval interactive retrieval system radiology imaging radiology report retrieval

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SparseOcc: Rethinking Sparse Latent Representation for vision-Based Semantic Occupancy Prediction

SparseOcc: Rethinking Sparse Latent Representation for Visio...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tang, Pin Wang, Zhongdao Wang, Gum-ling Zheng, Jilai Ren, Xiangxuan Feng, Bailan Ma, Chao Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.

关键词： Autonomous Driving Occupancy Prediction Sparse Representation vision-Based Perception

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