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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是81-90 订阅

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Improving Robustness of vision Transformers by Reducing Sensitivity to Patch Corruptions

Improving Robustness of Vision Transformers by Reducing Sens...

引用

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

作者： Guo, Yong Stutz, David Schiele, Bernt Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9798350301298

Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective - reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P.

关键词： Deep learning architectures and techniques

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ViTs for SITS: vision Transformers for Satellite Image Time Series

ViTs for SITS: Vision Transformers for Satellite Image Time ...

引用

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

作者： Tarasiou, Michail Chavez, Erik Zafeiriou, Stefanos Imperial Coll London London England

ISBN: (纸本)9798350301298

In this paper we introduce the Temporo-Spatial vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes can be found at https://***/michaeltrs/DeepSatModels.

关键词： Photogrammetry and remote sensing

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Generalizable Implicit Neural Representations via Instance pattern Composers

Generalizable Implicit Neural Representations via Instance P...

引用

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

作者： Kim, Chiheon Lee, Doyup Kim, Saehoon Cho, Minsu Han, Wook-Shin Kakao Brain Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer;the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

关键词： Self-supervised or unsupervised representation learning

来源：评论

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Learning to Detect and Segment for Open Vocabulary Object Detection

Learning to Detect and Segment for Open Vocabulary Object De...

引用

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

作者： Wang, Tao Sichuan Univ Chengdu Peoples R China

ISBN: (纸本)9798350301298

Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation.

关键词： detection recognition: Categorization retrieval

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Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

Adaptive Spot-Guided Transformer for Consistent Local Featur...

引用

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

作者： Yu, Jiahuan Chang, Jiahao He, Jianfeng Zhang, Tianzhu Yu, Jiyang Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Deep Space Explorat Lab Nanjing Jiangsu Peoples R China China Acad Space Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://***.

关键词： Low-level vision

来源：评论

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Neural Video Compression with Diverse Contexts

Neural Video Compression with Diverse Contexts

引用

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

作者： Li, Jiahao Li, Bin Lu, Yan Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://***/microsoft/DCVC.

关键词： Low-level vision

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vision Transformers Are Good Mask Auto-Labelers

Vision Transformers Are Good Mask Auto-Labelers

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

作者： Lan, Shiyi Yang, Xitong Yu, Zhiding Wu, Zuxuan Alvarez, Jose M. Anandkumar, Anima NVIDIA Santa Clara CA 95051 USA Meta AI FAIR London England Fudan Univ Shanghai Peoples R China CALTECH Pasadena CA USA

ISBN: (纸本)9798350301298

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

关键词： grouping and shape analysis Segmentation

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Are Data-driven Explanations Robust against Out-of-distribution Data?

Are Data-driven Explanations Robust against Out-of-distribut...

引用

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

作者： Li, Tang Qiao, Fenuchun Ma, Mengmeng Peng, Xi Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9798350301298

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts.

关键词： Explainable computer vision

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Constrained Evolutionary Diffusion Filter for Monocular Endoscope Tracking

Constrained Evolutionary Diffusion Filter for Monocular Endo...

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

作者： Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China

ISBN: (纸本)9798350301298

Stochastic filtering is widely used to deal with nonlinear optimization problems such as 3-D and visual tracking in various computer vision and augmented reality applications. Many current methods suffer from an imbalance between exploration and exploitation due to their particle degeneracy and impoverishment, resulting in local optimums. To address this imbalance, this work proposes a new constrained evolutionary diffusion filter for nonlinear optimization. Specifically, this filter develops spatial state constraints and adaptive history-recall differential evolution embedded evolutionary stochastic diffusion instead of sequential resampling to resolve the degeneracy and impoverishment problem. With application to monocular endoscope 3-D tracking, the experimental results show that the proposed filtering significantly improves the balance between exploration and exploitation and certainly works better than recent 3-D tracking methods. Particularly, the surgical tracking error was reduced from 4.03 mm to 2.59 mm.

关键词： cell microscopy Medical and biological vision

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Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action recognition from Egocentric RGB Videos

Hierarchical Temporal Transformer for 3D Hand Pose Estimatio...

引用

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

作者： Wen, Yilin Pan, Hao Yang, Lei Pan, Jia Komura, Taku Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China TransGP Hong Kong Peoples R China Texas A&M Univ College Stn TX USA

ISBN: (纸本)9798350301298

Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity. To address occlusion and ambiguity, we develop a transformer-based framework to exploit temporal information for robust estimation. Noticing the different temporal granularity of and the semantic correlation between hand pose estimation and action recognition, we build a network hierarchy with two cascaded transformer encoders, where the first one exploits the short-term temporal cue for hand pose estimation, and the latter aggregates per-frame pose and object information over a longer time span to recognize the action. Our approach achieves competitive results on two first-person hand action benchmarks, namely FPHA and H2O. Extensive ablation studies verify our design choices.

关键词： body gesture Humans: Face movement pose

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