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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4721-4730 订阅

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CRIS: CLIP-Driven Referring Image Segmentation

CRIS: CLIP-Driven Referring Image Segmentation

引用

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

作者： Wang, Zhaoqing Lu, Yu Li, Qiang Tao, Xunqiang Guo, Yandong Gong, Mingming Liu, Tongliang Univ Sydney Sydney NSW Australia OPPO Res Inst Dongguan Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Univ Melbourne Parkville Vic Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CUP), in this paper, we propose an end-to-end CUP-Driven Referring Image Segmentation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically;we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.

关键词： Representation learning Image segmentation Visualization computer vision Image analysis Shape Semantics

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Registration of diffusion tensor images

Registration of diffusion tensor images

引用

Proceedings of the 2004 ieee computer society conference on computer vision and pattern recognition, CVPR 2004

作者： Zhang, Hui Yushkevich, Paul A. Gee, James C. Department of Computer Science University of Pennsylvania Philadelphia PA 19104 Department of Radiology University of Pennsylvania Philadelphia PA 19104

This paper presents a novel affine registration algorithm for diffusion tensor images. The proposed metric derived from the standpoint of diffusion profiles not only has concrete physical underpinning but also can be extended for comparing higher-order diffusion models. The non-translauonal part of the affine transformation is parametrized in the spirit of the Polar Decomposition Theorem. The registration objective function and its derivatives are derived analytically by combining this parametrization scheme with finite strain tensor reorientation. The affine algorithm is embeded in a multi-resolution piecewise affine framework for non-rigid registration.

关键词： Image analysis

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Physical Property Understanding from Language-Embedded Feature Fields

Physical Property Understanding from Language-Embedded Featu...

引用

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

作者： Zhai, Albert J. Shen, Yuan Chen, Emily Y. Wang, Gloria X. Wang, Xinlei Wang, Sheng Guan, Kaiyu Wang, Shenlong Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9798350353006

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate, annotation-free, and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks, such as estimating the mass of common objects, as well as other properties like friction and hardness. Code is available at https://***/NeRF2Physics.

关键词： 3D scene understanding digital twin physical properties vision and language

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MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection

MSTR: Multi-Scale Transformer for End-to-End Human-Object In...

引用

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

作者： Kim, Bumsoo Mun, Jonghwan On, Kyoung-Woon Shin, Minchul Lee, Junhyun Kim, Eun-Sol LG AI Res Geumsan South Korea Kakao Brain Seongnam South Korea Korea Univ Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection is the task of identifying a set of (human, object, interaction) triplets from an image. Recent work proposed transformer encoder-decoder architectures that successfully eliminated the need for many hand-designed components in HOI detection through end-to-end training. However, they are limited to single-scale feature resolution, providing suboptimal performance in scenes containing humans, objects, and their interactions with vastly different scales and distances. To tackle this problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered by two novel HOIaware deformable attention modules called Dual-Entity attention and Entity-conditioned Context attention. While existing deformable attention comes at a huge cost in HOI detection performance, our proposed attention modules of MSTR learn to effectively attend to sampling points that are essential to identify interactions. In experiments, we achieve the new state-of-the-art performance on two HOI detection benchmarks.

关键词： Training Image resolution Semantics Detectors computer architecture Benchmark testing Transformers

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Transientboost: On-line boosting with transient data

Transientboost: On-line boosting with transient data

引用

2010 ieee computer society conference on computer vision and pattern recognition - Workshops, CVPRW 2010

作者： Sternig, Sabine Godec, Martin Roth, Peter M. Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9781424470297

For on-line learning algorithms, which are applied in many vision tasks such as detection or tracking, robust integration of unlabeled samples is a crucial point. Various strategies such as self-training, semi-supervised learning and multiple-instance learning have been proposed. However, these methods are either too adaptive, which causes drifting, or biased by a prior, which hinders incorporation of new (orthogonal) information. Therefore, we propose a new on-line learning algorithm (TransientBoost), which is highly adaptive but still robust. This is realized by using an internal multi-class representation and modeling reliable and unreliable data in separate classes. Unreliable data is considered transient, hence we use highly adaptive learning parameters to adapt to fast changes in the scene while errors fade out fast. In contrast, the reliable data is preserved completely and not harmed by wrong updates. We demonstrate our algorithm on two different tasks, i.e., object detection and object tracking showing that we can handle typical problems considerable better than existing approaches. To demonstrate the stability and the robustness, we show long-term experiments for both tasks. © 2010 ieee.

关键词： Object detection

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Explicit Occlusion Modeling for 3D Object Class Representations

Explicit Occlusion Modeling for 3D Object Class Representati...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Zia, M. Zeeshan Stark, Michael Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland Stanford Univ Stanford CA 94305 USA Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9780769549897

Despite the success of current state-of-the-art object class detectors, severe occlusion remains a major challenge. This is particularly true for more geometrically expressive 3D object class representations. While these representations have attracted renewed interest for precise object pose estimation, the focus has mostly been on rather clean datasets, where occlusion is not an issue. In this paper, we tackle the challenge of modeling occlusion in the context of a 3D geometric object class model that is capable of fine-grained, part-level 3D object reconstruction. Following the intuition that 3D modeling should facilitate occlusion reasoning, we design an explicit representation of likely geometric occlusion patterns. Robustness is achieved by pooling image evidence from of a set of fixed part detectors as well as a non-parametric representation of part configurations in the spirit of poselets. We confirm the potential of our method on cars in a newly collected data set of inner-city street scenes with varying levels of occlusion, and demonstrate superior performance in occlusion estimation and part localization, compared to baselines that are unaware of occlusions.

关键词： computer graphics image reconstruction image representation object detection pose estimation

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A-ViT: Adaptive Tokens for Efficient vision Transformer

A-ViT: Adaptive Tokens for Efficient Vision Transformer

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

作者： Yin, Hongxu Vahdat, Arash Alvarez, Jose M. Mallya, Arun Kautz, Jan Molchanov, Pavlo NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce A - ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. A - ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds. We refor-mulate Adaptive Computation Time (ACT [17]) for this task, extending halting to discard redundant spatial tokens. The appealing architectural properties of vision transformers enables our adaptive token reduction mechanism to speed up inference without modifying the network architecture or inference hardware. We demonstrate that A - ViT requires no extra parameters or sub-network for halting, as we base the learning of adaptive halting on the original network parameters. We further introduce distributional prior regularization that stabilizes training compared to prior ACT approaches. On the image classification task (ImageNet1K), we show that our proposed A - ViT yields high efficacy in filtering informative spatial features and cutting down on the overall compute. The proposed method improves the throughput of DeiT-Tiny by 62% and DeiT-Small by 38% with only 0.3% accuracy drop, outperforming prior art by a large margin.

关键词： Training Adaptive systems Network architecture Transformers Throughput Hardware Complexity theory

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Stereo coupled active contours

Stereo coupled active contours

引用

Proceedings of the 1997 ieee computer society conference on computer vision and pattern recognition

作者： Cham, Tat-Jen Cipolla, Roberto Univ of Cambridge Cambridge United Kingdom

We consider how tracking in stereo may be enhanced by coupling pairs of active contours in different views via affine epipolar geometry and various subsets of planar affine transformations, as well as by implementing temporal constraints imposed by curve rigidity. 3D curve tracking is achieved using a submanifold model, where it is shown how the coupling mechanisms can be decomposed to cater for fixed and variable epipolar geometries. In the case of tracking planar curves, the canonical frame model is developed such that the various geometrical constraints needed in different situations may be efficiently selected. The results show that coupled active contours add consistency and robustness to tracking in stereo.

关键词： Stereo vision

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Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning

Siamese Contrastive Embedding Network for Compositional Zero...

引用

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

作者： Li, Xiangyu Yang, Xu Wei, Kun Deng, Cheng Yang, Muli Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state;the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese Contrastive Embedding Network (SCEN)1 for unseen composition recognition. Considering the entanglement between state and object, we embed the visual feature into a Siamese Contrastive Space to capture prototypes of them separately, alleviating the interaction between state and object. In addition, we design a State Transition Module (STM) to increase the diversity of training compositions, improving the robustness of the recognition model. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three challenging benchmark datasets, including the recent proposed C-QGA dataset.

关键词： Training Deep learning Visualization computer vision Prototypes Training data Robustness

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DSRNA: Differentiable Search of Robust Neural Architectures

DSRNA: Differentiable Search of Robust Neural Architectures

引用

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

作者： Hosseini, Ramtin Yang, Xingyi Xie, Pengtao Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture to change prediction outcomes significantly. To address this problem, we propose methods to perform differentiable search of robust neural architectures. In our methods, two differentiable metrics are defined to measure architectures' robustness, based on certified lower bound and Jacobian norm bound. Then we search for robust architectures by maximizing the robustness metrics. Different from previous approaches which aim to improve architectures' robustness in an implicit way: performing adversarial training and injecting random noise, our methods explicitly and directly maximize robustness metrics to harvest robust architectures. On CIFAR-10, ImageNet, and MNIST, we perform game-based evaluation and verification-based evaluation on the robustness of our methods. The experimental results show that our methods 1) are more robust to various norm-bound attacks than several robust NAS baselines;2) are more accurate than baselines when there are no attacks;3) have significantly higher certified lower bounds than baselines.

关键词： Measurement Jacobian matrices Deep learning Training Perturbation methods computer architecture Search problems

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