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

共 12859 条记录，以下是291-300 订阅

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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

引用

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

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

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Pose Tutor: An Explainable System for Pose Correction in the Wild

Pose Tutor: An Explainable System for Pose Correction in the...

引用

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

作者： Dittakavi, Bhat Bavikadi, Divyagna Desai, Sai Vikas Chakraborty, Soumi Reddy, Nishant Balasubramanian, Vineeth N. Callepalli, Bharathi Sharma, Ayon IIT Hyderabad Hyderabad Telangana India Variance AI Hyderabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to hire a personal trainer. In this paper, we develop Pose Tutor, an AI-based explainable pose recognition and correction system. Pose Tutor combines vision and pose skeleton models in a novel coarse-to-fine framework to obtain pose class predictions. An angle-likelihood mechanism is used to explain which human joints maximally caused the pose class predictions and also correct any wrongly formed joints. Even without keypoint level training, Pose Tutor shows promising results on Yoga-82, Pilates-32, and Kungfu-7 datasets. Additionally, user studies conducted with multiple domain experts validate the explanations provided by our framework.

关键词： Training computer vision conferences Predictive models Muscles Skeleton pattern recognition

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Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Beyond VQA: Generating Multi-word Answers and Rationales to ...

引用

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

作者： Dua, Radhika Kancheti, Sai Srinivas Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9781665448994

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

关键词： Deep learning Visualization Vocabulary Computational modeling conferences computer architecture Knowledge discovery

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Towards CNN map representation and compression for camera relocalisation 31

Towards CNN map representation and compression for camera re...

引用

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

作者： Contreras, Luis Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocalisation results and evaluate the response to different data inputs. We use a CNN map representation and introduce the notion of map compression under this paradigm by using smaller CNN architectures without sacrificing relocalisation performance. We evaluate this approach in a series of publicly available datasets over a number of CNN architectures with different sizes, both in complexity and number of layers. This formulation allows us to improve relocalisation accuracy by increasing the number of training trajectories while maintaining a constant-size CNN.

关键词： Training Task analysis Cameras computer architecture Trajectory Biological neural networks

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SAM: Pushing the Limits of Saliency Prediction Models 31

SAM: Pushing the Limits of Saliency Prediction Models

引用

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

作者： Cornia, Marcella Baraldi, Lorenzo Serra, Giuseppe Cmcidara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Udine Udine Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

The prediction of human eye fixations has been recently gaining a lot of attention thanks to the improvements shown by deep architectures. In our work, we go beyond classical feed-forward networks to predict saliency maps and propose a Saliency Attentive Model which incorporates neural attention mechanisms to iteratively refine predictions. Experiments demonstrate that the proposed strategy overcomes by a considerable margin the state of the art on the largest dataset available for saliency prediction. Here, we provide experimental results on other popular saliency datasets to confirm the effectiveness and the generalization capabilities of our model, which enable us to reach the state of the art on all considered datasets.

关键词： Predictive models Visualization Measurement Feature extraction conferences computer architecture Computational modeling

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DeepRing: Protecting Deep Neural Network with Blockchain 32

DeepRing: Protecting Deep Neural Network with Blockchain

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Goel, Akhil Agarwal, Akshay Vatsa, Mayank Singh, Richa Ratha, Nalini IIIT Delhi Delhi NY 13753 USA IBM Res Ossining NY USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash, as well as symmetric/asymmetric encryption and decryption algorithms, ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.

关键词： Public key computer architecture Machine learning

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ViTOL: vision Transformer for Weakly Supervised Object Localization

ViTOL: Vision Transformer for Weakly Supervised Object Local...

引用

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

作者： Gupta, Saurav Lakhotia, Sourav Rawat, Abhay Tallamraju, Rahul Mercedes Benz Res & Dev India Bengaluru India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects are, (a) they tend to look at the most discriminative features in an image that confines the localization map to a very small region, (b) the localization maps are class agnostic, and the models highlight objects of multiple classes in the same image and, (c) the localization performance is affected by background noise. To alleviate the above challenges we introduce the following simple changes through our proposed method ViTOL. We leverage the vision-based transformer for self-attention and introduce a patch-based attention dropout layer (p-ADL) to increase the coverage of the localization map and a gradient attention rollout mechanism to generate class-dependent attention maps. We conduct extensive quantitative, qualitative and ablation experiments on the ImageNet-1K and CUB datasets. We achieve state-of-the-art MaxBoxAcc-V2 localization scores of 70.47% and 73.17% on the two datasets respectively.

关键词： Location awareness Measurement Visualization Shape Semantics computer architecture Transformers

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An Effective Temporal Localization Method with Multi-View 3D Action recognition for Untrimmed Naturalistic Driving Videos

An Effective Temporal Localization Method with Multi-View 3D...

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

作者： Manh Tung Tran Minh Quan Vu Ngoc Duong Hoang Khac-Hoai Nam Bui Viettel Grp Viettel Cyperspace Ctr Hanoi Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Naturalistic driving studies with computer vision techniques have become an emergent research issue. The objective is to classify the distracted behavior actions by drivers. Specifically, this issue is regarded as temporal action localization (TAL) of untrimmed videos, which is a challenging task in the research field of video analysis. Particularly, TAL remains as one of the most challenging unsolved problems in computer vision that requires not only the recognition of action but the localization of the start and end times of each action. Most state-of-the-art approaches adopt complex architectures, which are expensive training and inefficient inference time. In this study, we propose a new framework for untrimmed naturalistic driving videos by utilizing the results from 3D action recognition with video clip classification for short temporal and spatial correlation. Then, simple post-processing based on data-driven is presented for long temporal correlation in untrimmed videos. The proposed method is evaluated on the AI City Challenge 2022 dataset for Naturalistic Driving Action recognition. Accordingly, our method achieves the top 1 on the public leaderboard of the challenge.

关键词： Location awareness Training computer vision Solid modeling Correlation Urban areas X3D

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Anticipation of Human Actions with Pose-based Fine-grained Representations 32

Anticipation of Human Actions with Pose-based Fine-grained R...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Agethen, Sebastian Lee, Hu-Cheng Hsu, Winston H. Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9781728125060

Anticipating an action that is about to happen allows us to be more efficient in interacting with our environment. However, prediction is a challenging task in computer vision, because videos are only partially available when a decision is to be made. Complicating the issue is that it is not always clear which of the visible activities in the scene are relevant to the action, and which ones are not. We suggest that the key to recognizing an action lies with the human actors, and that it is therefore necessary for the prediction process to attend to persons in a scene. In our work, we extract fine-grained features on visible human actors and predict the future via an L2-regression in feature space. This allows the regressed future feature to focus on the actor. Using this, the future action is classified. More specifically, the fine-grained extraction is guided by a pose prediction system that models current and future human poses in the scene. We run qualitative and quantitative experiments on the Charades dataset, and initial results show that our system improves action prediction.

关键词： computer vision

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Dataset Distillation by Matching Training Trajectories

Dataset Distillation by Matching Training Trajectories

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.

关键词： Training Visualization computer vision conferences Trajectory Task analysis pattern matching

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