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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161-170 订阅

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Natural Language-Assisted Sign Language recognition

Natural Language-Assisted Sign Language Recognition

引用

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

作者： Zuo, Ronglai Wei, Fangyun Mak, Brian Hong Kong Univ Sci & Technol Hong Kong Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign languages, which limits the recognition capacity of vision neural networks. To mitigate the problem, we propose the Natural Language-Assisted Sign Language recognition (NLA-SLR) framework, which exploits semantic information contained in glosses (sign labels). First, for VISigns with similar semantic meanings, we propose language-aware label smoothing by generating soft labels for each training sign whose smoothing weights are computed from the normalized semantic similarities among the glosses to ease training. Second, for VISigns with distinct semantic meanings, we present an inter-modality mixup technique which blends vision and gloss features to further maximize the separability of different signs under the supervision of blended labels. Besides, we also introduce a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields. Empirically, our method achieves state-of-the-art performance on three widely-adopted benchmarks: MSASL, WLASL, and NMFs-CSL. Codes are available at https://***/FangyunWei/SLRT.

关键词： vision applications and systems

来源：评论

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Explaining Image Classifiers with Multiscale Directional Image Representation

Explaining Image Classifiers with Multiscale Directional Ima...

引用

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

作者： Kolek, Stefan Windesheim, Robert Andrade-Loarca, Hector Kutyniok, Gitta Levie, Ron Ludwig Maximilians Univ Munchen Dept Math Munich Germany Univ Tromso Dept Phys & Technol Tromso Norway Technion Israel Inst Technol Dept Math Haifa Israel

ISBN: (纸本)9798350301298

Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shear-let transform - a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constraints that protect against undesirable fine-grained explanation artifacts. However, the smoothness of a mask limits its ability to separate fine-detail patterns, that are relevant for the classifier, from nearby nuisance patterns, that do not affect the classifier. ShearletX solves this problem by avoiding smoothness regularization all together, replacing it by shearlet sparsity constraints. The resulting explanations consist of a few edges, textures, and smooth parts of the original image, that are the most relevant for the decision of the classifier. To support our method, we propose a mathematical definition for explanation artifacts and an information theoretic score to evaluate the quality of mask explanations. We demonstrate the superiority of ShearletX over previous mask based explanation methods using these new metrics, and present exemplary situations where separating fine-detail patterns allows explaining phenomena that were not explainable before.

关键词： Explainable computer vision

来源：评论

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Learning Steerable Function for Efficient Image Resampling

Learning Steerable Function for Efficient Image Resampling

引用

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

作者： Li, Jiacheng Chen, Chang Huang, Wei Lang, Zhiqiang Song, Fenglong Yan, Youliang Xiong, Zhiwei Univ Sci & Technol China Chengdu Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for x2 upsampling on Manga109.

关键词： Low-level vision

来源：评论

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Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

Breaching FedMD: Image Recovery via Paired-Logits Inversion ...

引用

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

作者： Takahashi, Hideaki Liu, Jingjing Liu, Yang Univ Tokyo Tokyo Japan Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Tsinghua Univ Inst AI Ind Res Shanghai Artificial Intelligence Lab Beijing Peoples R China

ISBN: (纸本)9798350301298

Federated Learning with Model Distillation (FedMD) is a nascent collaborative learning paradigm, where only output logits of public datasets are transmitted as distilled knowledge, instead of passing on private model parameters that are susceptible to gradient inversion attacks, a known privacy risk in federated learning. In this paper, we found that even though sharing output logits of public datasets is safer than directly sharing gradients, there still exists a substantial risk of data exposure caused by carefully designed malicious attacks. Our study shows that a malicious server can inject a PLI (Paired-Logits Inversion) attack against FedMD and its variants by training an inversion neural network that exploits the confidence gap between the server and client models. Experiments on multiple facial recognition datasets validate that under FedMD-like schemes, by using paired server-client logits of public datasets only, the malicious server is able to reconstruct private images on all tested benchmarks with a high success rate.

关键词： accountability ethics in vision fairness privacy Transparency

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Complexity-guided Slimmable Decoder for Efficient Deep Video Compression

Complexity-guided Slimmable Decoder for Efficient Deep Video...

引用

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

作者： Hu, Zhihao Xu, Dong Beihang Univ Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to automatically decide the optimal channel width of each slimmable convolution layer. By optimizing the complexity-rate-distortion related objective function to directly learn the parameters of the newly introduced channel width selection modules and other modules in the decoder, our cgSlimDecoder can automatically allocate the optimal numbers of parameters for different types of modules (e.g., motion/residual decoder and the motion compensation network) and simultaneously support multiple complexity levels by using a single learnt decoder instead of multiple decoders. In addition, our proposed SaEC can further accelerate the entropy decoding procedure in both motion and residual decoders by simply skipping the entropy coding process for the elements in the encoded feature maps that are already well-predicted by the hyperprior network. As demonstrated in our comprehensive experiments, our newly proposed methods cgSlimDecoder and SaEC are general and can be readily incorporated into three widely used deep video codecs (i.e., DVC, FVC and DCVC) to significantly improve their coding efficiency with negligible performance drop.

关键词： Low-level vision

来源：评论

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STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action recognition

STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Ac...

引用

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

作者： Zhu, Xiaoyu Huang, Po-Yao Liang, Junwei de Melo, Celso M. Hauptmann, Alexander Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta AI FAIR New York NY USA HKUST Guangzhou Guangzhou Peoples R China DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github. com/zgzxy001/STMT.

关键词： Video: Action and event understanding

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Decoupling MaxLogit for Out-of-Distribution Detection

Decoupling MaxLogit for Out-of-Distribution Detection

引用

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

作者： Zhang, Zihan Xiang, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9798350301298

In machine learning, it is often observed that standard training outputs anomalously high confidence for both indistribution (ID) and out-of-distribution (OOD) data. Thus, the ability to detect OOD samples is critical to the model deployment. An essential step for OOD detection is post-hoc scoring. MaxLogit is one of the simplest scoring functions which uses the maximum logits as OOD score. To provide a new viewpoint to study the logit-based scoring function, we reformulate the logit into cosine similarity and logit norm and propose to use MaxCosine and MaxNorm. We empirically find that MaxCosine is a core factor in the effectiveness of MaxLogit. And the performance of MaxLogit is encumbered by MaxNorm. To tackle the problem, we propose the Decoupling MaxLogit (DML) for flexibility to balance MaxCosine and MaxNorm. To further embody the core of our method, we extend DML to DML+ based on the new insights that fewer hard samples and compact feature space are the key components to make logit-based methods effective. We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance.

关键词： detection recognition: Categorization retrieval

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Referring Multi-Object Tracking

Referring Multi-Object Tracking

引用

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

作者： Wu, Dongming Han, Wencheng Wang, Tiancai Dong, Xingping Zhang, Xiangyu Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Univ Macau SKL IOTSC CIS Macau Peoples R China MEGVII Technol Beijing Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Beijing Acad Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The Refer-KITTI dataset and the code are released at https://***.

关键词： and reasoning language vision

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ScaleDet: A Scalable Multi-Dataset Object Detector

ScaleDet: A Scalable Multi-Dataset Object Detector

引用

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

作者： Chen, Yanbei Wang, Manchen Mittal, Abhay Xu, Zhenlin Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset detector (ScaleDet) that can scale up its generalization across datasets when increasing the number of training datasets. Unlike existing multi-dataset learners that mostly rely on manual relabelling efforts or sophisticated optimizations to unify labels across datasets, we introduce a simple yet scalable formulation to derive a unified semantic label space for multi-dataset training. ScaleDet is trained by visual-textual alignment to learn the label assignment with label semantic similarities across datasets. Once trained, ScaleDet can generalize well on any given upstream and downstream datasets with seen and unseen classes. We conduct extensive experiments using LVIS, COCO, Objects365, OpenImages as upstream datasets, and 13 datasets from Object Detection in the Wild (ODinW) as downstream datasets. Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50.7 on LVIS, 58.8 on COCO, 46.8 on Objects365, 76.2 on OpenImages, and 71.8 on ODinW, surpassing state-of-the-art detectors with the same backbone.

关键词： Efficient and scalable vision

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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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