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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11889 条记录，以下是161-170 订阅

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Unravelling Robustness of Deep Face recognition Networks Against Illicit Drug Abuse Images

Unravelling Robustness of Deep Face Recognition Networks Aga...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Dhake, Hruturaj Agarwal, Akshay IISER Bhopal Data Sci & Engn Bhopal India

ISBN: (纸本)9798350365474

Alteration in facial features can lead to a significant drop in recognition performance. These alterations can be due to several factors: one such prominent and less explored factor is illicit drug abuse. To advance the understanding of how drug abuse faces affect the performance of state-of-the-art deep face recognition (DFR) networks, in this study, we have utilized clean and illicit drug abuse faces. Extensive studies are performed on deep face recognition and soft biometric identification, such as gender, ethnicity, and expression recognition. It is observed that illicit drug abuse not only impacts the identity recognition performance but also degrades the soft biometrics identification accuracy. Therefore, to advance the integrity of DFR, we have performed the detection of illicit drug abuse as a potential solution to its mitigation. In the end, the robustness of the drug abuse face detector is evaluated under the prominent use of social-media filters on face images.

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Iterated Learning Improves Compositionality in Large vision-Language Models

Iterated Learning Improves Compositionality in Large Vision-...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Chenhao Zhang, Jieyu Kembhavi, Aniruddha Krishna, Ranjay Univ Washington Seattle WA 98195 USA Univ Michigan Ann Arbor MI 48109 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9798350353006

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, recent investigations find that most-if not all-our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover, prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission-the need to teach a new generation-as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically, we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent, and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration, this training paradigm induces representations that become "easier to learn", a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7%, 4.0% respectfully in the SugarCrepe benchmark.

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lap Quoc Tran Huan Duc Vi Asilla Tokyo Japan

ISBN: (纸本)9798350365474

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision

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SonicvisionLM: Playing Sound with vision Language Models

SonicVisionLM: Playing Sound with Vision Language Models

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xie, Zhifeng Yu, Shengye He, Qile Li, Mengtian Shanghai Univ Shanghai Peoples R China Shanghai Engn Res Ctr Mot Picture Special Effects Shanghai Peoples R China

ISBN: (纸本)9798350353006

There has been a growing interest in the task of generating sound for silent videos, primarily because of its practicality in streamlining video post-production. However, existing methods for video-sound generation attempt to directly create sound from visual representations, which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper, we present SonicvisionLM, a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video, we use the capabilities of powerful VLMs. When provided with a silent video, our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs, we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio, enhancing synchronization with the visuals, and improving alignment between audio and video components. Project page: https://***. io/***/

关键词： audio generation multi-modal vision-language model

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RMem: Restricted Memory Banks Improve Video Object Segmentation

RMem: Restricted Memory Banks Improve Video Object Segmentat...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Junbao Pang, Ziqi Wang, Yu-Xiong Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios, we revisit a sim-ple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of ex-panding memory banks to accommodate extensive histor-ical information. Our specially designed memory deci-phering study offers a pivotal insight underpinning such a strategy: expanding memory banks, while seemingly bene-ficial, actually increases the difficulty for VOS modules to decode relevant features due to the confusion from redun-dant information. By restricting memory banks to a limited number of essential frames, we achieve a notable improvement in VOS accuracy. This process balances the im-portance and freshness of frames to maintain an informative memory bank within a bounded capacity. Additionally, restricted memory banks reduce the training-inference discrepancy in memory lengths compared with continuous expansion. This fosters new opportunities in temporal reasoning and enables us to introduce the previously overlooked temporal positional embedding. Finally, our insights are embodied in RMem (R for restricted), a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (on the VOST dataset) and long videos (on the Long Videos dataset). Our code and demos are available at https://***/.

关键词： egocentric vision embodied ai video object segmentation video understanding

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Unlocking the Potential of Pre-trained vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors

Unlocking the Potential of Pre-trained Vision Transformers f...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Ziqin Xu, Hai-Ming Shu, Yangyang Liu, Lingqiao Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350353013;9798350353006

The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper, we explore to harnesses this emergent feature to tackle few-shot semantic segmentation, a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery, we find that the concept of "relationship descriptors", initially conceived for enhancing the CLIP model for zero-shot semantic segmentation, offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation, extending its application across multiple layers to enhance performance. Building upon this adaptation, we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets, including PASCAL-5(i) and COCO-20(i), we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios, and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques, highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://***/ZiqinZhou66/***.

关键词： Semantic Segmentation

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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Mitigating Object Hallucinations in Large vision-Language Models through Visual Contrastive Decoding

Mitigating Object Hallucinations in Large Vision-Language Mo...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Leng, Sicong Zhang, Hang Chen, Guanzheng Li, Xin Lug, Shijian Miao, Chunyan Bing, Lidong Alibaba Grp DAMO Acad Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Large vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success, LVLMs still suffer from the issue of object hallucinations, where models generate plausible yet incorrect outputs that include objects that do not exist in the images. To mitigate this issue, we introduce Visual Contrastive Decoding (VCD), a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors, two essential causes of object hallucinations. This adjustment ensures the generated content is closely grounded to visual inputs, resulting in contextually accurate outputs. Our experiments show that VCD, without either additional training or the usage of external tools, significantly mitigates the object hallucination issue across different LVLM families. Beyond mitigating object hallucinations, VCD also excels in general LVLM benchmarks, highlighting its wide-ranging applicability.

关键词： Large Multimodal Models Multimodality vision and Language

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BigGait: Learning Gait Representation You Want by Large vision Models

BigGait: Learning Gait Representation You Want by Large Visi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ye, Dingqiang Fan, Chao Ma, Jingzhe Liu, Xiaoming Yu, Shiqi Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Michigan State Univ E Lansing MI USA

ISBN: (纸本)9798350353013;9798350353006

Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However, existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences, which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend, this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large vision Models (LVMs) and proposes a simple yet efficient gait framework, termed BigGait. Specifically, the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations, effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG, CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases, and provides a more practical paradigm for learning the next-generation gait representation. Finally, we delve into prospective challenges and promising directions in LVMs-based gait recognition, aiming to inspire future work in this emerging topic. The source code is available at https://github. com/ShiqiYu/OpenGait.

关键词： Gait recognition Large vision Models Person Re-Identification

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Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation

Exploring Regional Clues in CLIP for Zero-Shot Semantic Segm...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Yi Guo, Meng-Hao Wang, Miao Hu, Shi-Min Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However, it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper, to solve the mentioned challenge, we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations, we propose a novel method for zero-shot semantic segmentation, dubbed CLIP-RC (CLIP with Regional Clues), bringing two main insights. On the one hand, a region-level bridge is necessary to provide fine-grained semantics. On the other hand, overfitting should be mitigated during the training stage. Benefiting from the above discoveries, CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks, including PASCAL VOC, PASCAL Context, and COCO-Stuff 164K. Code will be available at https://***/Jittor/JSeg.

关键词： computer vision semantic segmentation zero-shot semantic segmentation

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