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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4131-4140 订阅

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Coarse-Fine Networks for Temporal Activity Detection in Videos

Coarse-Fine Networks for Temporal Activity Detection in Vide...

引用

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

作者： Kahatapitiya, Kumara Ryoo, Michael S. SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665445092

In this paper, we introduce Coarse-Fine Networks, a two-stream architecture which benefits from different abstractions of temporal resolution to learn better video representations for long-term motion. Traditional Video models process inputs at one (or few) fixed temporal resolution without any dynamic frame selection. However, we argue that, processing multiple temporal resolutions of the input and doing so dynamically by learning to estimate the importance of each frame can largely improve video representations, specially in the domain of temporal activity localization. To this end we propose (1) 'Grid Pool', a learned temporal downsampling layer to extract coarse features, and, (2) 'Multi-stage Fusion', a spatio-temporal attention mechanism to fitse a fine-grained context with the coarse features. We show that our method outperforms the state-of-the-arts for action detection in public datasets including Charades with a significantly reduced compute and memory footprint.

关键词： Location awareness computer vision Codes Fuses Dynamics computer architecture Feature extraction

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MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

MultiLink: Multi-class Structure Recovery via Agglomerative ...

引用

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

作者： Magri, Luca Leveni, Filippo Boracchi, Giacomo Politecn Milan DEIB Milan Italy

ISBN: (纸本)9781665445092

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that MultiLink favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download(1).

关键词： Couplings computer vision Codes Fitting Clustering algorithms Tools Data models

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Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

Ego-Exo: Transferring Visual Representations from Third-pers...

引用

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

作者： Li, Yanghao Nagarajan, Tushar Xiong, Bo Grauman, Kristen Facebook AI Res Mountain View CA 94127 USA UT Austin Austin TX USA

ISBN: (纸本)9781665445092

We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signals in third-person video that are predictive of key egocentric-specific properties. Incorporating these signals as knowledge distillation losses during pre-training results in models that benefit from both the scale and diversity of third-person video data, as well as representations that capture salient egocentric properties. Our experiments show that our "Ego-Exo" framework can be seamlessly integrated into standard video models;it outperforms all baselines when fine-tuned for egocentric activity recognition, achieving state-of-the-art results on Charades-Ego and EPIC-Kitchens-100.

关键词： Training Visualization computer vision Computational modeling Pipelines Activity recognition Data models

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Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer

Lipstick ain't enough: Beyond Color Matching for In-the-Wild...

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

作者： Thao Nguyen Anh Tuan Tran Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

Makeup transfer is the task of applying on a source face the makeup style from a reference image. Real-life makeups are diverse and wild, which cover not only color-changing but also patterns, such as stickers, blushes, and jewelries. However, existing works overlooked the latter components and confined makeup transfer to color manipulation, focusing only on light makeup styles. In this work, we propose a holistic makeup transfer framework that can handle all the mentioned makeup components. It consists of an improved color transfer branch and a novel pattern transfer branch to learn all makeup properties, including color, shape, texture, and location. To train and evaluate such a system, we also introduce new makeup datasets for real and synthetic extreme makeup. Experimental results show that our framework achieves the state of the art performance on both light and extreme makeup styles. Code is available at https://***/VinAIResearch/CPM.

关键词： computer vision Codes Image color analysis Shape Face recognition Focusing Task analysis

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Faster Meta Update Strategy for Noise-Robust Deep Learning

Faster Meta Update Strategy for Noise-Robust Deep Learning

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

作者： Xu, Youjiang Zhu, Linchao Jiang, Lu Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this paper, we introduce a novel Faster Meta Update Strategy (FaMUS) to replace the most expensive step in the meta gradient computation with a faster layer-wise approximation. We empirically find that FaMUS yields not only a reasonably accurate but also a low-variance approximation of the meta gradient. We conduct extensive experiments to verify the proposed method on two tasks. We show our method is able to save two-thirds of the training time while still maintaining the comparable or achieving even better generalization performance. In particular, our method achieves the state-of-the-art performance on both synthetic and realistic noisy labels, and obtains promising performance on long-tailed recognition on standard benchmarks.

关键词： Training Deep learning computer vision Training data Robustness pattern recognition Noise robustness

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Dynamic Head: Unifying Object Detection Heads with Attentions

Dynamic Head: Unifying Object Detection Heads with Attention...

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

作者： Dai, Xiyang Chen, Yinpeng Xiao, Bin Chen, Dongdong Liu, Mengchen Yuan, Lu Zhang, Lei Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781665445092

The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. The code will be released at http://***/microsoft/DynamicHead.

关键词： Location awareness computer vision Codes Computational modeling Object detection Detectors Feature extraction

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Correspondence Transformers with Asymmetric Feature Learning and Matching Flow Super-Resolution

Correspondence Transformers with Asymmetric Feature Learning...

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conference on computer vision and pattern recognition (cvpr)

作者： Yixuan Sun Dongyang Zhao Zhangyue Yin Yiwen Huang Tao Gui Wenqiang Zhang Weifeng Ge Academy of Engineering & Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

This paper solves the problem of learning dense visual correspondences between different object instances of the same category with only sparse annotations. We decompose this pixel-level semantic matching problem into two easier ones: (i) First, local feature descriptors of source and target images need to be mapped into shared semantic spaces to get coarse matching flows. (ii) Second, matching flows in low resolution should be refined to generate accurate point-to-point matching results. We propose asymmetric feature learning and matching flow super-resolution based on vision transformers to solve the above problems. The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts. Then matching flow in low resolutions is enhanced by a super-resolution network to get accurate correspondences. Our pipeline is built upon vision transformers and can be trained in an end-to-end manner. Extensive experimental results on several popular benchmarks, such as PF-PASCAL, PF-WILLOW, and SPair-71 K, demonstrate that the proposed method can catch subtle semantic differences in pixels efficiently. Code is available on https://***/YXSUNMADMAX/ACTR.

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Understanding Failures of Deep Networks via Robust Feature Extraction

Understanding Failures of Deep Networks via Robust Feature E...

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

作者： Singla, Sahil Nushi, Besmira Shah, Shital Kamar, Ece Horvitz, Eric Univ Maryland College Pk MD 20742 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. In distinction to previous work that relies upon crowdsourced labels for visual attributes, we leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes. We further propose a visualization method aimed at enabling humans to understand the meaning encoded in such features and we test the comprehensibility of the features. An evaluation of the methods on the ImageNet dataset demonstrates that: (i) the proposed workflow is effective for discovering important failure modes, (ii) the visualization techniques help humans to understand the extracted features, and (iii) the extracted insights can assist engineers with error analysis and debugging.

关键词： Measurement Visualization computer vision Error analysis Aggregates Debugging Feature extraction

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IMAGINE: Image Synthesis by Image-Guided Model Inversion

IMAGINE: Image Synthesis by Image-Guided Model Inversion

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

作者： Wang, Pei Li, Yijun Singh, Krishna Kumar Lu, Jingwan Vasconcelos, Nuno UC San Diego CA 92093 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We leverage the knowledge of image semantics from a pre-trained classifier to achieve plausible generations via matching multi-level feature representations in the classifier, associated with adversarial training with an external discriminator. IMAGINE enables the synthesis procedure to simultaneously 1) enforce semantic specificity constraints during the synthesis, 2) produce realistic images without generator training, and 3) give users intuitive control over the generation process. With extensive experimental results, we demonstrate qualitatively and quantitatively that IMAGINE performs favorably against state-of-the-art GAN-based and inversion-based methods, across three different image domains (i.e., objects, scenes, and textures).

关键词： Training computer vision Image synthesis Computational modeling Semantics Process control Generators

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Long-Tailed Multi-Label Visual recognition by Collaborative Training on Uniform and Re-balanced Samplings

Long-Tailed Multi-Label Visual Recognition by Collaborative ...

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

作者： Guo, Hao Wang, Song Univ South Carolina Columbia SC 29201 USA

ISBN: (纸本)9781665445092

Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the rebalanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition.

关键词： Training Visualization computer vision Head Collaboration Collaborative work pattern recognition

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