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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30988 条记录，以下是4621-4630 订阅

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Strategies to Leverage Foundational Model Knowledge in Object Affordance Grounding

Strategies to Leverage Foundational Model Knowledge in Objec...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Arushi Rai Kyle Buettner Adriana Kovashka Department of Computer Science University of Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

An important task for intelligent systems is affordance grounding, where the goal is to locate regions on an object where an action can be performed. Past weakly supervised approaches learn from human-object interaction (HOI) by transferring grounding knowledge from exocentric to ego-centric views of an object. The use of HOI priors is inherently noisy and thus provides a limited source of supervision. To address this challenge, we identify that recent foundational models (i.e. VLMs and LLMs) can serve as auxiliary sources of knowledge for frameworks due to their vast world knowledge. In this work, we propose strategies to extract and leverage foundational model knowledge related to attributes and object parts to enhance an HOI-based affordance grounding framework. In particular, we propose to combine HOI and foundational model priors through (1) a spatial consistency loss and (2) heatmap aggregation. Our strategies result in mKLD and mNSS improvements, and insights suggest future directions for improving affordance grounding capabilities.

关键词： Heating systems computer vision Grounding Affordances conferences Pipelines pattern recognition

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Predicting Human Scanpaths in Visual Question Answering

Predicting Human Scanpaths in Visual Question Answering

引用

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

作者： Chen, Xianyu Jiang, Ming Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781665445092

Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploration is a temporal process that contributes to task performance. To bridge the gap, we conduct a first study to understand and predict the temporal sequences of eye fixations (a.k.a. scanpaths) during performing general tasks, and examine how scanpaths affect task performance. We present a new deep reinforcement learning method to predict scanpaths leading to different performances in visual question answering. Conditioned on a task guidance map, the proposed model learns question-specific attention patterns to generate scanpaths. It addresses the exposure bias in scanpath prediction with self-critical sequence training and designs a Consistency-Divergence loss to generate distinguishable scanpaths between correct and incorrect answers. The proposed model not only accurately predicts the spatio-temporal patterns of human behavior in visual question answering, such as fixation position, duration, and order, but also generalizes to free-viewing and visual search tasks, achieving human-level performance in all tasks and significantly outperforming the state of the art.

关键词： Training Visualization computer vision Reinforcement learning Predictive models Tools Knowledge discovery

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Indirect synthetic attack on thermal face biometric systems via visible-to-thermal spectrum conversion

Indirect synthetic attack on thermal face biometric systems ...

引用

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

作者： Mallat, Khawla Dugelay, Jean-Luc EURECOM Sophia Antipolis France

ISBN: (纸本)9781665448994

The growing interest in exploring thermal face biometrics is mostly due to the robustness of thermal imagery to face spoofing attacks. However, this robustness lies in the acquisition of thermal properties by the thermal sensor and is limited to presentation attacks. In this paper, we propose a new type of attack on thermal face recognition systems, performed at the post-sensor level. In the visible spectrum, this attack would be carried out by simply injecting a face image of the claimed identity into the communication channel right after the sensor. However, unlike visible face images that are abundantly available on the web, thermal face images are not easy to obtain. Therefore, we propose to generate synthetic thermal attacks by converting visible face images into the thermal spectrum. To perform visible-to-thermal spectrum conversion, we use a cascaded refinement network trained using contextual loss. In a scenario where the attacker has prior knowledge about the spoofing countermeasure of the system, we introduce a new loss computed at the local binary pattern (LBP) maps level to fool an LBP-based spoofing attack detection algorithm. The vulnerability of thermal face biometric systems to the proposed attack is then assessed using two existing baselines of spoofing attack detection. When compared to the challenging presentation attack using silicone masks, the equal error rate has increased from 0.20% to 11.60% and from 2.28% to 58.54% when exposed to the proposed synthetic attack, using the two spoofing attack detection baselines.

关键词： computer vision Error analysis Social networking (online) Face recognition conferences Communication channels Thermal sensors

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An analysis of best-practice strategies for replay and rehearsal in continual learning

An analysis of best-practice strategies for replay and rehea...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Alexander Krawczyk Alexander Gepperth Applied Computer Science Department University of Applied Sciences Fulda Fulda Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study is in the context of class-incremental continual learning using replay, which has seen notable progress in recent years, fueled by concepts like conditional, latent or maximally interfered replay. However, there are many design choices to take when it comes to implementing replay strategies, with potentially very different outcomes in the various class-incremental scenarios. Some of the obvious design choices in replay are the use of experience replay (ER), the use of different generators like GANs –vs– VAEs for generative replay, or whether to re-initialize generators after each task. For replay strategies in general, it is an open question how many samples to generate for each new task, and what weights to give generated and new samples in the loss. On top of this, there are many possible CL evaluation protocols differing in the amount of tasks, the balancing of tasks or fundamental complexity (e.g., MNIST -vs- latent CIFAR/SVHN), and thus few generic conclusions about best practices for replay/rehearsal have found consensus in the literature. This study aims at establishing such best-practices by conducting an extensive set of representative replay experiments.

关键词： Continuing education computer vision Protocols conferences Generators pattern recognition Complexity theory

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Augmented Self-Mask Attention Transformer for Naturalistic Driving Action recognition

Augmented Self-Mask Attention Transformer for Naturalistic D...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Tiantian Zhang Qingtian Wang Xiaodong Dong Wenqing Yu Hao Sun Xuyang Zhou Aigong Zhen Shun Cui Dong Wu Zhongjiang He China Telecom Artificial Intelligence Technology (Beijing) Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, naturalistic driving action recognition and computer vision techniques provide crucial solutions to identify and eliminate distracting driving behavior. Existing methods often extract features through fixed-size sliding windows and predict an action’s start and end time. However, the information about a fixed-size window may be incomplete or redundant and the connections between different windows are insufficient. To alleviate this problem, we propose a novel Augmented Self-Mask Attention (AMA) architecture that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order. We employ an ensemble technique and use a weighted boundaries fusion to combine and refine predictions with high confidence scores action boundaries. On the test dataset of AI City Challenge 2024 Track3, we achieved significant results compared with other teams, the proposed model ranks first on the public leaderboard of the challenge. Codes are available at https://***/wolfworld6/AIcity2024-track3.

关键词： computer vision conferences Computational modeling Urban areas computer architecture Feature extraction Transformers

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Large-capacity Image Steganography Based on Invertible Neural Networks

Large-capacity Image Steganography Based on Invertible Neura...

引用

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

作者： Lu, Shao-Ping Wang, Rong Zhong, Tao Rosin, Paul L. Nankai Univ CS TKLNDST Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781665445092

Many attempts have been made to hide information in images, where one main challenge is how to increase the payload capacity without the container image being detected as containing a message. In this paper, we propose a large-capacity Invertible Steganography Network (ISN) for image steganography. We take steganography and the recovery of hidden images as a pair of inverse problems on image domain transformation, and then introduce the forward and backward propagation operations of a single invertible network to leverage the image embedding and extracting problems. Sharing all parameters of our single ISN architecture enables us to efficiently generate both the container image and the revealed hidden image(s) with high quality. Moreover, in our architecture the capacity of image steganography is significantly improved by naturally increasing the number of channels of the hidden image branch. Comprehensive experiments demonstrate that with this significant improvement of the steganography payload capacity, our ISN achieves state-of-the-art in both visual and quantitative comparisons.

关键词： Backpropagation Steganography Visualization computer vision Inverse problems Neural networks computer architecture

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Automated Log-Scale Quantization for Low-Cost Deep Neural Networks

Automated Log-Scale Quantization for Low-Cost Deep Neural Ne...

引用

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

作者： Oh, Sangyun Sim, Hyeonuk Lee, Sugil Lee, Jongeun UNIST Dept Elect Engn Ulsan South Korea UNIST Dept Comp Sci & Engn Ulsan South Korea

ISBN: (纸本)9781665445092

Quantization plays an important role in deep neural network (DNN) hardware. In particular, logarithmic quantization has multiple advantages for DNN hardware implementations, and its weakness in terms of lower performance at high precision compared with linear quantization has been recently remedied by what we call selective two-word logarithmic quantization (STLQ). However, there is a lack of training methods designed for STLQ or even logarithmic quantization in general. In this paper we propose a novel STLQ-aware training method, which significantly outperforms the previous state-of-the-art training method for STLQ. Moreover, our training results demonstrate that with our new training method, STLQ applied to weight parameters of ResNet-18 can achieve the same level of performance as state-of-the-art quantization method, APoT, at 3-bit precision. We also apply our method to various DNNs in image enhancement and semantic segmentation, showing competitive results.

关键词： Training Deep learning Image segmentation computer vision Quantization (signal) Semantics computer architecture

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Self-supervised Video Representation Learning by Context and Motion Decoupling

Self-supervised Video Representation Learning by Context and...

引用

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

作者： Huang, Lianghua Liu, Yu Wang, Bin Pan, Pan Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. While most existing works implicitly achieve this with video-specific pretext tasks (e.g., predicting clip orders, time arrows, and paces), we develop a method that explicitly decouples motion supervision from context bias through a carefully designed pretext task. Specifically, we take the key frames and motion vectors in compressed videos (e.g., in H.264 format) as the supervision sources for context and motion, respectively, which can be efficiently extracted at over 500 fps on CPU. Then we design two pretext tasks that are jointly optimized: a context matching task where a pairwise contrastive loss is cast between video clip and key frame features;and a motion prediction task where clip features, passed through an encoderdecoder network, are used to estimate motion features in a near future. These two tasks use a shared video backbone and separate MLP heads. Experiments show that our approach improves the quality of the learned video representation over previous works, where we obtain absolute gains of 16.0% and 11.1% in video retrieval recall on UCF101 and HMDB51, respectively. Moreover, we find the motion prediction to be a strong regularization for video networks, where using it as an auxiliary task improves the accuracy of action recognition with a margin of 7.4% similar to 13.8%.

关键词： computer vision Feature extraction pattern recognition Task analysis

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Self-supervised Motion Learning from Static Images

Self-supervised Motion Learning from Static Images

引用

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

作者： Huang, Ziyuan Zhang, Shiwei Jiang, Jianwen Tang, Mingqian Jin, Rong Ang, Marcelo H., Jr. Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distinguish the actions, especially those with complicated spatio-temporal interactions, correctly locating the prominent motion areas is of crucial importance. However, most motion information in existing videos are difficult to label and training a model with good motion representations with supervision will thus require a large amount of human labour for annotation. In this paper, we address this problem by self-supervised learning. Specifically, we propose to learn Motion from Static Images (MoSI). The model learns to encode motion information by classifying pseudo motions generated by MoSI. We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets. As a result, the learned motion representations boost the performance of tasks requiring understanding of complex scenes and motions, i.e., action recognition. Extensive experiments show the consistent and transferable improvements achieved by MoSI. Codes will be soon released.

关键词： Training computer vision Codes Annotations pattern recognition Task analysis Videos

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Peekaboo: Interactive Video Generation via Masked-Diffusion

Peekaboo: Interactive Video Generation via Masked-Diffusion

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yash Jain Anshul Nasery Vibhav Vineet Harkirat Behl Microsoft University of Washington Project Webpage

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However, a significant limitation is their inability to offer interactive control to users, a feature that promises to open up unprecedented applications and creativity. In this work, we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present PEEKABOO, a novel masked attention module, which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research, we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that PEEKABOO achieves up to a 3.8x improvement in mIoU over baseline models, all while maintaining the same latency. Code and benchmark are available on the webpage.

关键词： Training computer vision Codes Computational modeling Benchmark testing pattern recognition Creativity

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