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共 11753 条记录，以下是181-190 订阅

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Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection

Optimal Proposal Learning for Deployable End-to-End Pedestri...

引用

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

作者： Song, Xiaolin Chen, Binghui Li, Pengyu He, Jun-Yan Wang, Biao Geng, Yifeng Xie, Xuansong Zhang, Honggang Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods.

关键词： detection recognition: Categorization retrieval

来源：评论

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DINER: Disorder-Invariant Implicit Neural Representation

DINER: Disorder-Invariant Implicit Neural Representation

引用

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

作者： Xie, Shaowen Zhu, Hao Liu, Zhen Zhang, Qi Zhou, You Cao, Xun Ma, Zhan Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Tencent Co Al Lab Shenzhen 518054 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the capacity of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be largely solved by re-arranging the coordinates of the input signal, for which we propose the disorder-invariant implicit neural representation (DINER) by augmenting a hash-table to a traditional INR backbone. Given discrete signals sharing the same histogram of attributes and different arrangement orders, the hash-table could project the coordinates into the same distribution for which the mapped signal can be better modeled using the subsequent INR network, leading to significantly alleviated spectral bias. Experiments not only reveal the generalization of the DINER for different INR backbones (MLP vs. SIREN) and various tasks (image/video representation, phase retrieval, and refractive index recovery) but also show the superiority over the state-of-the-art algorithms both in quality and speed. Project page: https://***/DINER-website/

关键词： Low-level vision

来源：评论

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Mobile User Interface Element Detection Via Adaptively Prompt Tuning

Mobile User Interface Element Detection Via Adaptively Promp...

引用

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

作者： Gu, Zhangxuan Xu, Zhuoer Chen, Haoxing Lan, Jun Meng, Changhua Wang, Weiqiang Tiansuan Lab Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI element detection dataset named MUI-zh and propose an Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR information. APT is a lightweight and effective module to jointly optimize category prompts across different modalities. For every element, APT uniformly encodes its visual features and OCR descriptions to dynamically adjust the representation of frozen category prompts. We evaluate the effectiveness of our plug-and-play APT upon several existing CLIP-based detectors for both standard and open-vocabulary MUI element detection. Extensive experiments show that our method achieves considerable improvements on two datasets. The datasets is available at github. com/antmachineintelligence/MUI-zh.

关键词： language reasoning vision

来源：评论

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Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Diffusion Art or Digital Forgery? Investigating Data Replica...

引用

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

作者： Somepalli, Gowthami Singla, Vasu Goldblum, Micah Geiping, Jonas Goldstein, Tom Univ Maryland College Pk MD 20742 USA NYU New York NY USA

ISBN: (纸本)9798350301298

Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data. Project page: https://***/***

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face recognition

Discrete Point-wise Attack Is Not Enough: Generalized Manifo...

引用

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

作者： Li, Qian Hu, Yuxiao Liu, Ye Zhang, Dongxiao Jin, Xin Chen, Yuntian Eastern Inst Adv Study Ningbo Zhejiang Peoples R China

ISBN: (纸本)9798350301298

Classical adversarial attacks for Face recognition (FR) models typically generate discrete examples for target identity with a single state image. However, such paradigm of point-wise attack exhibits poor generalization against numerous unknown states of identity and can be easily defended. In this paper, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA)(1) to achieve a better attack performance by expanding the attack range. Specifically, this expansion lies on two aspects - GMAA not only expands the target to be attacked from one to many to encourage a good generalization ability for the generated adversarial examples, but it also expands the latter from discrete points to manifold by leveraging the domain knowledge that face expression change can be continuous, which enhances the attack effect as a data augmentation mechanism did. Moreover, we further design a dual supervision with local and global constraints as a minor contribution to improve the visual quality of the generated adversarial examples. We demonstrate the effectiveness of our method based on extensive experiments, and reveal that GMAA promises a semantic continuous adversarial space with a higher generalization ability and visual quality.

关键词： Adversarial attack and defense

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Enhanced Training of Query-Based Object Detection via Selective Query Recollection

Enhanced Training of Query-Based Object Detection via Select...

引用

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

作者： Chen, Fangyi Zhang, Han Hu, Kai Huang, Yu-Kai Zhu, Chenchen Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI San Francisco CA USA

ISBN: (纸本)9798350301298

This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4 similar to 2.8 AP improvement. Code is available at https://***/Fangyi-Chen/SQR

关键词： detection recognition: Categorization retrieval

来源：评论

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Recurrent Homography Estimation Using Homography-Guided Image Warping and Focus Transformer

Recurrent Homography Estimation Using Homography-Guided Imag...

引用

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

作者： Cao, Si-Yuan Zhang, Runmin Luo, Lun Yu, Beinan Sheng, Zehua Li, Junwei Shen, Hui-Liang Zhejiang Univ Ningbo Innovat Ctr Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350301298

We propose the Recurrent homography estimation framework using Homography-guided image Warping and Focus transformer (FocusFormer), named RHWF. Both being appropriately absorbed into the recurrent framework, the homography-guided image warping progressively enhances the feature consistency and the attention-focusing mechanism in FocusFormer aggregates the intra-inter correspondence in a global -> nonlocal -> local manner. Thanks to the above strategies, RHWF ranks top in accuracy on a variety of datasets, including the challenging cross-resolution and cross-modal ones. Meanwhile, benefiting from the recurrent framework, RHWF achieves parameter efficiency despite the transformer architecture. Compared to previous state-of-the-art approaches LocalTrans and IHN, RHWF reduces the mean average corner error (MACE) by about 70% and 38.1% on the MSCOCO dataset, while saving the parameter costs by 86.5% and 24.6%. Similar to the previous works, RHWF can also be arranged in 1-scale for efficiency and 2-scale for accuracy, with the 1-scale RHWF already outperforming most of the previous methods. Source code is available at https://***/imdumpl78/RHWF.

关键词： Low-level vision

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Learning Rotation-Equivariant Features for Visual Correspondence

Learning Rotation-Equivariant Features for Visual Correspond...

引用

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

作者： Lee, Jongmin Kim, Byungjin Kim, Seungwook Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation.

关键词： Low-level vision

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GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for vision-and-Language Navigation

GeoVLN: Learning Geometry-Enhanced Visual Representation wit...

引用

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

作者： Huo, Jingyang Sun, Qiang Jiang, Boyan Lin, Haitao Fu, Yanwei Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to model merely with cross attention. In this paper, we propose GeoVLN, which learns Geometry-enhanced visual representation based on slot attention for robust Visual-and-Language Navigation. The RGB images are compensated with the corresponding depth maps and normal maps predicted by Omnidata as visual inputs. Technically, we introduce a two-stage module that combine local slot attention and CLIP model to produce geometry-enhanced representation from such input. We employ V&L BERT to learn a cross-modal representation that incorporate both language and vision informations. Additionally, a novel multiway attention module is designed, encouraging different phrases of input instruction to exploit the most related features from visual input. Extensive experiments demonstrate the effectiveness of our newly designed modules and show the compelling performance of the proposed method.

关键词： Multi-modal learning

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MobileOne: An Improved One millisecond Mobile Backbone

MobileOne: An Improved One millisecond Mobile Backbone

引用

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

作者： Vasu, Pavan Kumar Anasosalu Gabriel, James Zhu, Jeff Tuzel, Oncel Ranjan, Anurag Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350301298

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device. Code and models are available at https://***/apple/ml-mobileone

关键词： Efficient and scalable vision

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