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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4661-4670 订阅

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Localized Adversarial Domain Generalization

Localized Adversarial Domain Generalization

引用

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

作者： Zhu, Wei Lu, Le Xiao, Jing Han, Mei Luo, Jiebo Harrison, Adam P. PAII Inc Palo Alto CA 94306 USA Univ Rochester Rochester NY 14627 USA Alibaba DAMO Acad Beijing Peoples R China PingAn Insurance Grp Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning methods can struggle to handle domain shifts not seen in training data, which can cause them to not generalize well to unseen domains. This has led to research attention on domain generalization (DG), which aims to the model's generalization ability to out-of-distribution. Adversarial domain generalization is a popular approach to DG, but conventional approaches (1) struggle to sufficiently align features so that local neighborhoods are mixed across domains;and (2) can suffer from feature space over collapse which can threaten generalization performance. To address these limitations, we propose localized adversarial domain generalization with space compactness maintenance (LADG) which constitutes two major contributions. First, we propose an adversarial localized classifier as the domain discriminator, along with a principled primary branch. This constructs a min-max game whereby the aim of the featurizer is to produce locally mixed domains. Second, we propose to use a coding-rate loss to alleviate feature space over collapse. We conduct comprehensive experiments on the Wilds DG benchmark to validate our approach, where LADG outperforms leading competitors on most datasets.

关键词： Deep learning computer vision Training data Games Benchmark testing Maintenance engineering pattern recognition

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How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?

How does topology influence gradient propagation and model p...

引用

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

作者： Bhardwaj, Kartikeya Li, Guihong Marculescu, Radu Arm Inc San Jose CA 95134 USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

DenseNets introduce concatenation-type skip connections that achieve state-of-the-art accuracy in several computer vision tasks. In this paper, we reveal that the topology of the concatenation-type skip connections is closely related to the gradient propagation which, in turn, enables a predictable behavior of DNNs' test performance. To this end, we introduce a new metric called NN-Mass to quantify how effectively information flows through DNNs. Moreover, we empirically show that NN-Mass also works for other types of skip connections, e.g., for ResNets, Wide-ResNets (WRNs), and MobileNets, which contain addition-type skip connections (i.e., residuals or inverted residuals). As such, for both DenseNet-like CNNs and ResNets/WRNs/MobileNets, our theoretically grounded NN-Mass can identify models with similar accuracy, despite having significantly different size/compute requirements. Detailed experiments on both synthetic and real datasets (e.g., MNIST, CIFAR-10, CIFAR100, ImageNet) provide extensive evidence for our insights. Finally, the closed-form equation of our NN-Mass enables us to design significantly compressed DenseNets (for CIFAR10) and MobileNets (for ImageNet) directly at initialization without time-consuming training and/or searching.(1)

关键词： Measurement Training computer vision Image coding Artificial neural networks Mathematical models Topology

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Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence

Telling Left from Right: Identifying Geometry-Aware Semantic...

引用

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

作者： Zhang, Junyi Herrmann, Charles Hur, Junhwa Chen, Eric Jampani, Varun Sun, Deqing Yang, Ming-Hsuan Shanghai Jiao Tong Univ Shanghai Peoples R China Google Res Mountain View CA USA UIUC Champaign IL USA Stabil AI London England UC Merced Merced CA USA

ISBN: (纸本)9798350353013;9798350353006

While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset, for both pre-training validating models. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset, surpassing the state of the art by 5.5p and 11.0p absolute gains, respectively. Our code and datasets are publicly available at: https://***

关键词： diffusion models semantic correspondence vision transformer

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Invertible Denoising Network: A Light Solution for Real Noise Removal

Invertible Denoising Network: A Light Solution for Real Nois...

引用

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

作者： Liu, Yang Qin, Zhenyue Anwar, Saeed Ji, Pan Kim, Dongwoo Caldwell, Sabrina Gedeon, Tom Australian Natl Univ Canberra ACT Australia CSIRO Data61 Canberra ACT Australia OPPO US Res Palo Alto CA USA GSAI POSTECH Pohang South Korea

ISBN: (纸本)9781665445092

Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distributions. We propose an invertible denoising network, InvDN, to address this challenge. InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise. To discard noise and restore the clean image, InvDN replaces the noisy latent representation with another one sampled from a prior distribution during reversion. The denoising performance of InvDN is better than all the existing competitive models, achieving a new state-of-the-art result for the SIDD dataset while enjoying less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of the number of parameters compared to the most recently proposed DANet. Further, via manipulating the noisy latent representation, InvDN is also able to generate noise more similar to the original one. Our code is available at: https://***/Yang-Liu1082/***.

关键词： computer vision Codes Computational modeling Noise reduction Transforms Image restoration pattern recognition

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Occluded Human Mesh Recovery

Occluded Human Mesh Recovery

引用

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

作者： Khirodkar, Rawal Tripathi, Shashank Kitani, Kris Carnegie Mellon Univ Pittsburgh PA 15213 USA Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes;(2) treat each bounding box as an independent single-human mesh recovery task. Unfortunately, the single-human assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down methods have difficulties in recovering accurate 3D human meshes under severe person-person occlusion. To address this, we present Occluded Human Mesh Recovery (OCHMR) - a novel top-down mesh recovery approach that incorporates image spatial context to overcome the limitations of the single-human assumption. The approach is conceptually simple and can be applied to any existing top-down architecture. Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps. To reason from the predicted body centermaps, we introduce Contextual Normalization (CoNorm) blocks to adaptively modulate intermediate features of the top-down model. The contextual conditioning helps our model disambiguate between two severely overlapping human bounding-boxes, making it robust to multi-person occlusion. Compared with state-of-the-art methods, OCHMR achieves superior performance on challenging multi-person benchmarks like 3DPW, CrowdPose and OCHuman. Specifically, our proposed contextual reasoning architecture applied to the SPIN model with ResNet-50 backbone results in 75.2 PMPJPE on 3DPW-PC, 23.6 AP on CrowdPose and 37.7 AP on OCHu-man datasets, a significant improvement of 6.9 mm, 6.4 AP and 20.8 AP respectively over the baseline.

关键词： Heating systems Adaptation models computer vision Three-dimensional displays computer architecture Predictive models pattern recognition

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Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

Abstract Spatial-Temporal Reasoning via Probabilistic Abduct...

引用

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

作者： Zhang, Chi Jia, Baoxiong Zhu, Song-Chun Zhu, Yixin UCLA Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA

ISBN: (纸本)9781665445092

Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based on spatial-temporal knowledge in mind, and an applied requirement on a high-level cognitive system capable of navigating and acting in space and time. Recent works have focused on an abstract reasoning task of this kind-Raven's Progressive Matrices (RPM). Despite the encouraging progress on RPM that achieves human-level performance in terms of accuracy, modern approaches have neither a treatment of human-like reasoning on generalization, nor a potential to generate answers. To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner;central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects. Specifically, we disentangle perception and reasoning from a monolithic model. The neural visual perception frontend predicts objects' attributes, later aggregated by a scene inference engine to produce a probabilistic scene representation. In the symbolic logical reasoning backend, the PrAE learner uses the representation to abduce the hidden rules. An answer is predicted by executing the rules on the probabilistic representation. The entire system is trained end-to-end in an analysis-by-synthesis manner without any visual attribute annotations. Extensive experiments demonstrate that the PrAE learner improves cross-configuration generalization and is capable of rendering an answer, in contrast to prior works that merely make a categorical choice from candidates.

关键词： Visualization computer vision Navigation Probabilistic logic Rendering (computer graphics) Cognition pattern recognition

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NIPQ: Noise proxy-based Integrated Pseudo-Quantization

NIPQ: Noise proxy-based Integrated Pseudo-Quantization

引用

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

作者： Shin, Juncheol So, Junhyuk Park, Sein Kang, Seungyeop Yoo, Sungjoo Park, Eunhyeok POSTECH Dept Comp Sci & Engn Pohang South Korea POSTECH Grad Sch Artificial Intelligence Pohang South Korea Seoul Natl Univ Dept ent Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350301298

Straight-through estimator (STE), which enables the gradient flow over the non-differentiable function via approximation, has been favored in studies related to quantization-aware training (QAT). However, STE incurs unstable convergence during QAT, resulting in notable quality degradation in low precision. Recently, pseudo-quantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE. In this study, we propose a novel noise proxy-based integrated pseudo-quantization (NIPQ) that enables unified support of pseudoquantization for both activation and weight by integrating the idea of truncation on the pseudo-quantization framework. NIPQ updates all of the quantization parameters (e.g., bit-width and truncation boundary) as well as the network parameters via gradient descent without STE instability. According to our extensive experiments, NIPQ outperforms existing quantization algorithms in various vision and language applications by a large margin.

关键词： Efficient and scalable vision

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Appearance-based object recognition using multiple views

Appearance-based object recognition using multiple views

引用

2001 ieee computer society conference on computer vision and pattern recognition

作者： Selinger, Andrea Nelson, Randal C. Department of Computer Science University of Rochester Rochester NY 14627 United States

Object recognition from a single view fails when the available features are not sufficient to determine the identity of a single object, either because of similarity with another object or because of feature corruption due to clutter and occlusion. Active object recognition systems have addressed this problem successfully, but they require complicated systems with adjustable viewpoints that are not always available. In this paper we investigate the performance gain available by combining the results of a single view object recognition system applied to imagery obtained from multiple fixed cameras. In particular, we address performance in cluttered scenes with varying degrees of information about relative camera pose. We argue that a property common to many computer vision recognition systems, which we term a weak target error, is responsible for two interesting limitations of multi-view performance enhancement: the lack of significant improvement in systems whose single-view performance is weak, and the plateauing of performance improvement as additional multi-view constraints are added.

关键词： Object recognition

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Subspace Adversarial Training

Subspace Adversarial Training

引用

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

作者： Li, Tao Wu, Yingwen Chen, Sizhe Fang, Kun Huang, Xiaolin Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e., the robust accuracy against projected gradient descent (PGD) attack suddenly drops to 0% during the training. In this paper, we approach this problem from a novel perspective of optimization and firstly reveal the close link between the fast-growing gradient of each sample and overfitting, which can also be applied to understand robust overfitting in multi-step AT. To control the growth of the gradient, we propose a new AT method, Subspace Adversarial Training (Sub-AT), which constrains AT in a carefully extracted subspace. It successfully resolves both kinds of overfitting and significantly boosts the robustness. In subspace, we also allow single-step AT with larger steps and larger radius, further improving the robustness performance. As a result, we achieve state-of-the-art single-step AT performance. Without any regularization term, our single-step AT can reach over 51% robust accuracy against strong PGD-50 attack of radius 8/255 on CIFAR-10, reaching a competitive performance against standard multi-step PGD-10 AT with huge computational advantages. The code is released at https://***/nblt/Sub-AT.

关键词： Training computer vision Codes Solids Robustness pattern recognition Standards

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VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

VideoINR: Learning Video Implicit Neural Representation for ...

引用

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

作者： Chen, Zeyuan Chen, Yinbo Liu, Jingwen Xu, Xingqian Goel, Vidit Wang, Zhangyang Shi, Humphrey Wang, Xiaolong USTC Hefei Peoples R China Univ Calif San Diego La Jolla CA 92093 USA UIUC Champaign IL 61820 USA UT Austin Austin TX USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate temporal interpolation and spatial super-resolution in a unified framework. However;most of them only support a fixed upsampling scale, which limits their flexibility and applications. In this work, instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), and we show its applications for STVSR. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. Our project page is at here and code is available at https://***/Picsart-AI-Research/VideoINR-Continuous-Space-Tune-Super-Resolution.

关键词： Visualization Interpolation computer vision Costs Codes Superresolution pattern recognition

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