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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 11890 条记录，以下是361-370 订阅

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Gated Fields: Learning Scene Reconstruction from Gated Videos

Gated Fields: Learning Scene Reconstruction from Gated Video...

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

作者： Ramazzina, Andrea Walz, Stefanie Dahal, Pragyan Bijelic, Mario Heide, Felix Mercedes Benz Stuttgart Germany Saarland Univ Saarbrucken Germany Politecn Milan Milan Italy Torc Robot Blacksburg VA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes with scanning LiDAR sensors is also difficult due to their low angular sampling rate which makes recovering expansive real-world scenes difficult. Tackling these gaps, we introduce Gated Fields - a neural scene reconstruction method that utilizes active gated video sequences. To this end, we propose a neural rendering approach that seamlessly incorporates time-gated capture and illumination. Our method exploits the intrinsic depth cues in the gated videos, achieving precise and dense geometry reconstruction irrespective of ambient illumination conditions. We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods. Our code and datasets are available here(1).

关键词： 3D vision Gated Camera Neural Radiance Fields Neural Rendering

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PracticalDG: Perturbation Distillation on vision-Language Models for Hybrid Domain Generalization

PracticalDG: Perturbation Distillation on Vision-Language Mo...

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

作者： Chen, Zining Wang, Weiqiu Zhao, Zhicheng Su, Fei Men, Aidong Meng, Hongying Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Minist Culture & Tourism Key Lab Interact Technol & Experience Syst Beijing Peoples R China Brunel Univ Uxbridge Uxbridge Middx England

ISBN: (纸本)9798350353006

Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless, there exists unseen classes from target domains in practical scenarios. To address this issue, Open Set Domain Generalization (OSDG) has emerged and several methods have been exclusively proposed. However, most ex-isting methods adopt complex architectures with slight improvement compared with DG methods. Recently, vision-language models (VLMs) have been introduced in DG following the fine-tuning paradigm, but consume huge training overhead with large vision models. Therefore, in this paper, we innovate to transfer knowledge from VLMs to lightweight vision models and improve the robustness by introducing Perturbation Distillation (PD) from three perspectives, including Score, Class and Instance (SCI), named SCI- PD. Moreover, previous methods are oriented by the benchmarks with identical and fixed splits, ignoring the divergence between source domains. These methods are revealed to suffer from sharp performance decay with our proposed new benchmark Hybrid Domain Generalization (HDG) and a novel metric H-2-CV, which construct various splits to comprehensively assess the robustness of algorithms. Extensive experiments demonstrate that our method outperforms state-of-the-art algorithms on multiple datasets, especially improving the robustness when con-fronting data scarcity.

关键词： Visual languages

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GLID: Pre-training a Generalist Encoder-Decoder vision Model

GLID: Pre-training a Generalist Encoder-Decoder Vision Model

引用

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

作者： Liu, Jihao Zheng, Jinliang Liu, Yu Li, Hongsheng CUHK MMLab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China CPII InnoHK Hong Kong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Shanghai Peoples R China

ISBN: (纸本)9798350353006

This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approaches, e.g., Masked Autoencoder, have shown success in transfer learning, task-specific sub-architectures are still required to be appended for different downstream tasks, which cannot enjoy the benefits of large-scale pre-training. GLID overcomes this challenge by allowing the pre-trained generalist encoder-decoder to be fine-tuned on various vision tasks with minimal task-specific architecture modifications. In the GLID training scheme, pre-training pretext task and other downstream tasks are modeled as "query-to-answer" problems, including the pre-training pretext task and other downstream tasks. We pre-train a task-agnostic encoder-decoder with query-mask pairs. During fine-tuning, GLID maintains the pre-trained encoder-decoder and queries, only replacing the topmost linear transformation layer with task-specific linear heads. This minimizes the pretrain-finetune architecture inconsistency and enables the pre-trained model to better adapt to downstream tasks. GLID achieves competitive performance on various vision tasks, including object detection, image segmentation, pose estimation, and depth estimation, outper-forming or matching specialist models such as Mask2Former, DETR, ViTPose, and BinsFormer.

关键词： Image segmentation

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Discovering and Mitigating Visual Biases through Keyword Explanation

Discovering and Mitigating Visual Biases through Keyword Exp...

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

作者： Kim, Younghyun Mo, Sangwoo Kim, Minkyu Lee, Kyungmin Lee, Jaeho Shin, Jinwoo Korea Adv Inst Sci & Technol Daejeon South Korea Univ Michigan Ann Arbor MI 48109 USA KRAFTON Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350353006

Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often identified indirectly through visualization or sample statistics, which necessitates additional human supervision for interpretation. To tackle this issue, we propose the Bias-to-Text (B2T) framework, which interprets visual biases as keywords. Specifically, we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases, such as gender bias in CelebA, background bias in Waterbirds, and distribution shifts in ImageNet-R/C. Additionally, B2T uncovers novel biases in larger datasets, such as Dollar Street and ImageNet. For example, we discovered a contextual bias between "bee" and "flower" in ImageNet. We also highlight various applications of B2T keywords, including debiased training, CLIP prompting, and model comparison.(1)

关键词： bias and fairness explainable AI vision-language model

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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

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

作者： Xu, Yuehuan Jiang, Shuai Cui, Zhe Su, Fei Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (纸本)9798350365474

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Action recognition Deep learning Distracted driver behavior

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Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimo...

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

作者： Tong, Shengbang Liu, Zhuang Zhai, Yuexiang Ma, Yi Lecun, Yann Xie, Saining NYU New York NY 10003 USA Meta FAIR Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798350353006

Is vision good enough for language? Recent advancements in multimodal models primarily stem from the powerful reasoning abilities of large language models (LLMs). However, the visual component typically depends only on the instance-level contrastive language-image pre-training ( CLIP). Our research reveals that the visual capabilities in recent MultiModal LLMs (MLLMs) still exhibit systematic shortcomings. To understand the roots of these errors, we explore the gap between the visual embedding space of CLIP and vision-only self-supervised learning. We identify "CLIP-blind pairs" - images that CLIP perceives as similar despite their clear visual differences. With these pairs, we construct the Multimodal Visual patterns (MMVP) benchmark. MMVP exposes areas where state-of-the-art systems, including GPT-4V, struggle with straightforward questions across nine basic visual patterns, often providing incorrect answers and hallucinated explanations. We further evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs. As an initial effort to address these issues, we propose a Mixture of Features (MoF) approach, demonstrating that integrating vision self-supervised learning features with MLLMs can significantly enhance their visual grounding capabilities. Together, our research suggests visual representation learning remains an open challenge, and accurate visual grounding is crucial for future successful multimodal systems.

关键词： Multimodal LLMs vision Language Model

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ST2ST: Self-Supervised Test-time Adaptation for Video Action recognition

ST2ST: Self-Supervised Test-time Adaptation for Video Action...

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

作者： Fahim, Masud An-Nur Islam Innat, Mohammed Boutellier, Jani Univ Vaasa Vaasa Finland Khulna Univ Engn & Technol KUET Khulna Bangladesh

ISBN: (纸本)9798350365474

The performance of trained deep neural network (DNN) models relies on the assumption that the test data has largely the same feature distribution as the training data. In deployed video recognition systems, the feature distribution of acquired samples can however become shifted due to environmental conditions (rain, lighting variations) or technological factors such as lossy data compression. To improve action recognition performance under feature distribution shifts, we propose a simple test-time self-distillation strategy where the DNN model goes through an intra-video logit minimization phase. As a result, the model can update its predictions for the given input. The proposed approach is agnostic to the neural network type (CNN, transformer) and applies to various action recognition models. In contrast to many test-time adaption studies, the proposed approach does not require access to the training data. The performance of the proposed method is evaluated with multiple state-of-the-art action recognition models and widely used benchmark datasets Kinetics-400 and Something-Something V2.

关键词： Action recogniton Self-Supervised TTA

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EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

EfficientSAM: Leveraged Masked Image Pretraining for Efficie...

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

作者： Xiong, Yunyang Varadarajan, Bala Wu, Lemeng Xiang, Xiaoyu Xiao, Fanyi Zhu, Chenchen Dai, Xiaoliang Wang, Dilin Sun, Fei Iandola, Forrest Krishnamoorthi, Raghuraman Chandra, Vikas Meta AI Res Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353006

Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial, the huge computation cost of SAM model has limited its applications to wider real-world applications. To address this limitation, we propose EfficientSAMs, lightweight SAM models that exhibits decent performance with largely reduced complexity. Our idea is based on leveraging masked image pretraining, SAMI, which learns to reconstruct features from SAM image encoder for effective visual representation learning. Further, we take SAMI-pretrained light-weight image encoders and mask decoder to build EfficientSAMs, and finetune the models on SA-1B for segment anything task. We perform evaluations on multiple vision tasks including image classification, object detection, instance segmentation, and semantic segmentation, and find that our proposed pretraining method, SAMI, consistently outperforms other masked image pretraining methods. On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e.g., similar to 4 AP on COCO/LVIS) over other fast SAM models. Our EfficientSAM code and models are available at here.

关键词： EfficientSAM Masked Image Pretraining SAMI Segment Anything vision Transformer

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Learning from One Continuous Video Stream

Learning from One Continuous Video Stream

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

作者： Carreira, Joao King, Michael Patraucean, Viorica Gokal, Dilara Ionescu, Cristian Yang, Yi Zoran, Daniel Heyward, Joseph Doersch, Carl Aytar, Yusuf Damen, Di Liu Zisserman, Andrew Google DeepMind London 1 England Univ Bristol Bristol Avon England Univ Oxford Oxford England

ISBN: (纸本)9798350353006

We introduce a framework for online learning from a single continuous video stream - the way people and animals learn, without mini-batches, data augmentation or shuffling. This poses great challenges given the high correlation between consecutive video frames and there is very little prior work on it. Our framework allows us to do a first deep dive into the topic and includes a collection of streams and tasks composed from two existing video datasets, plus methodology for performance evaluation that considers both adaptation and generalization. We employ pixel-to-pixel modelling as a practical and flexible way to switch between pre-training and single-stream evaluation as well as between arbitrary tasks, without ever requiring changes to models and always using the same pixel loss. Equipped with this framework we obtained large single-stream learning gains from pre-training with a novel family of future prediction tasks, found that momentum hurts, and that the pace of weight updates matters. The combination of these insights leads to matching the performance of IID learning with batch size 1, when using the same architecture and without costly replay buffers. An overview of the paper is available online at https://***/view/one-stream-video.

关键词： computer vision continual learning developmental learning

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Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

Alpha-CLIP: A CLIP Model Focusing on Wherever You Want

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

作者： Sun, Zeyi Fang, Ye Wu, Tong Zhang, Pan Zang, Yuhang Kong, Shu Xiong, Yuanjun Lin, Dahua Wang, Jiaqi Shanghai Jiao Tong Univ Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Macau Taipa Macao Peoples R China MThreads Inc Beijing Peoples R China

ISBN: (纸本)9798350353006

Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models. To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks. Our project is with codes and models available is linked to https://***/alpha-clip/.

关键词： CLIP MLLMs vision-language pretraining

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