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共 11890 条记录，以下是331-340 订阅

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Language-aware Visual Semantic Distillation for Video Question Answering

Language-aware Visual Semantic Distillation for Video Questi...

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

作者： Zou, Bo Yang, Chao Qiao, Yu Quan, Chengbin Zhao, Youjian Tsinghua Univ Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Significant progress in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although image-language models can efficiently represent both video and language branches, they typically employ goal-free vision perception and do not interact vision with language well during the answer generation, thus omitting crucial visual cues. In this paper, we are inspired by the human recognition and learning pattern and propose VideoDistill, a framework with language-aware (i.e., goal-driven) behavior in both vision perception and answer generation. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior, distinguishing it from previous research. Specifically, we develop a language-aware gating mechanism to replace the standard cross-attention, avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module, which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure extracting multi-grained visual semantics associated with the questions. We conduct evaluations on various challenging video question-answering benchmarks, and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition, we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.

关键词： Multi-modal Fusion Question-Answering Video Understanding VideoQA vision-Language

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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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GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis

GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting...

引用

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

作者： Zheng, Shunyuan Zhou, Boyao Shao, Ruizhi Liu, Boning Zhang, Shengping Nie, Liqiang Liu, Yebin Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353006

We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed. The code is available at https://***/aipixel/GPS-Gaussian.

关键词： Rendering (computer graphics)

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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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Collaborative Visual Place recognition through Federated Learning

Collaborative Visual Place Recognition through Federated Lea...

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

作者： Dutto, Mattia Berton, Gabriele Caldarola, Debora Fani, Eros Trivigno, Gabriele Masone, Carlo Politecn Torino Turin Italy

ISBN: (纸本)9798350365474

Visual Place recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural networks to extract a global representation, called descriptor, from each image. While the training data for VPR models often originates from diverse, geographically scattered sources (geo-tagged images), the training process itself is typically assumed to be centralized. This research revisits the task of VPR through the lens of Federated Learning (FL), addressing several key challenges associated with this adaptation. VPR data inherently lacks well-defined classes, and models are typically trained using contrastive learning, which necessitates a data mining step on a centralized database. Additionally, client devices in federated systems can be highly heterogeneous in terms of their processing capabilities. The proposed FedVPR framework not only presents a novel approach for VPR but also introduces a new, challenging, and realistic task for FL research, paving the way to other image retrieval tasks in FL.

关键词： Federated learning

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DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection

DiffAM: Diffusion-based Adversarial Makeup Transfer for Faci...

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

作者： Sun, Yuhao Yu, Lingyun Xie, Hongtao Li, Jiaming Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

With the rapid development of face recognition (FR) systems, the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However, the generated adversarial examples, i.e., the protected face images, tend to suffer from subpar visual quality and low transferability. In this paper, we propose a novel face protection approach, dubbed DiffAM, which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific, we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer, makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then, with this relationship, a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain, achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://***/HansSunY/DiffAM.

关键词： adversarial attack diffusion models face recognition

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Purposeful Regularization with Reinforcement Learning for Facial Expression recognition In-the-Wild

Purposeful Regularization with Reinforcement Learning for Fa...

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

作者： Hong, SangHwa Seoul Natl Univ Sci & Technol Dept Ind Engn Gongreung Ro 232 Seoul South Korea

ISBN: (纸本)9798350365474

Facial Expression recognition (FER), an essential aspect of emotion analysis through artificial intelligence, is a crucial research area. Although traditional approaches utilizing Convolutional Neural Networks (CNNs) for analyzing human emotions from facial expressions achieve superior accuracy over conventional machine learning methods, overfitting -especially arising severely from data collected in uncontrolled, In-the-wild settings -significantly impedes CNNs performance. This is due to the data scarcity and inherent noise inside In-the-wild data. To address this challenge, this paper introduces a novel regularization method that employs Reinforcement Learning (RL) to adaptively apply regularization hyperparameters appropriate for the evolving state of trained CNNs. Through experiments on various datasets such as CIFAR100, FER2013, and Affect-Net datasets including diverse perspective analysis such as graphical, Grad-CAM and numerical analysis, it is demonstrated that the suggested method can alleviate memorization of noise in training data and promote learning of essential features. The significance of the suggested method lies in its demonstrated remarkable effectiveness in enhancing CNNs' generalization and reducing overfitting.

关键词： FER Overfitting Reinforcement Learning

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OTE: Exploring Accurate Scene Text recognition Using One Token

OTE: Exploring Accurate Scene Text Recognition Using One Tok...

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

作者： Xu, Jianjun Wang, Yuxin Xie, Hongtao Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

In this paper, we propose a novel framework to fully exploit the potential of a single vector for scene text recognition (STR). Different from previous sequence-to-sequence methods that rely on a sequence of visual tokens to represent scene text images, we prove that just one token is enough to characterize the entire text image and achieve accurate text recognition. Based on this insight, we introduce a new paradigm for STR, called One Token rEcognizer (OTE). Specifically, we implement an image-to-vector encoder to extract the fine-grained global semantics, eliminating the need for sequential features. Furthermore, an elegant yet potent vector-to-sequence decoder is designed to adaptively diffuse global semantics to corresponding character locations, enabling both autoregressive and non-autoregressive decoding schemes. By executing decoding within a high-level representational space, our vector-to-sequence (V2S) approach avoids the alignment issues between visual tokens and character embeddings prevalent in traditional sequence-to-sequence methods. Remarkably, due to introducing character-wise fine-grained information, such global tokens also boost the performance of scene text retrieval tasks. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of our method by achieving new state-of-the-art results on various public STR benchmarks. Our code is available at https://***/Xu-Jianjun/OTE.

关键词： CLIP Scene Text recognition Scene Text Retrieval

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Zero-Reference Low-Light Enhancement via Physical Quadruple Priors

Zero-Reference Low-Light Enhancement via Physical Quadruple ...

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

作者： Wang, Wenjing Yang, Huan Fu, Jianlong Liu, Jiaying Peking Univ Beijing Peoples R China 01 AI Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350353006

Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle unseen scenarios. In this paper, we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this, we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then, we develop a prior-to-image framework trained without low-light data. During testing, this frame-work is able to restore our illumination-invariant prior back to images, automatically achieving low-light enhancement. Within this framework, we leverage a pretrained generative diffusion model for model ability, introduce a bypass decoder to handle detail distortion, as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability, robustness, and efficiency. Code is available on our project homepage.

关键词： diffusion image processing low-level vision Low-light enhancement zero-reference

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Open-Set Domain Adaptation for Semantic Segmentation

Open-Set Domain Adaptation for Semantic Segmentation

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

作者： Choe, Seun-An Shin, Ah-Hyung Park, Keon-Hee Choi, Jinwoo Park, Gyeong-Moon Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798350353006

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, current UDA methods typically assume a shared label space between source and target, limiting their applicability in real-world scenarios where novel categories may emerge in the target domain. In this paper, we introduce Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) for the first time, where the target domain includes unknown classes. We identify two major problems in the OSDA-SS scenario as follows: 1) the existing UDA methods struggle to predict the exact boundary of the unknown classes, and 2) they fail to accurately predict the shape of the unknown classes. To address these issues, we propose Boundary and Unknown Shape-Aware open-set domain adaptation, coined BUS. Our BUS can accurately discern the boundaries between known and unknown classes in a contrastive manner using a novel dilation-erosion-based contrastive loss. In addition, we propose OpenReMix, a new domain mixing augmentation method that guides our model to effectively learn domain and size-invariant features for improving the shape detection of the known and unknown classes. Through extensive experiments, we demonstrate that our proposed BUS effectively detects unknown classes in the challenging OSDA-SS scenario compared to the previous methods by a large margin. The code is available at https://***/KHU-AGI/BUS.

关键词： Contrastive Learning Domain Adaptation Domain Mixing Augmentation Open-Set recognition Semantic Segmentation

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