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DarkLight Networks for Action recognition in the Dark

DarkLight Networks for Action Recognition in the Dark

引用

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

作者： Chen, Rui Chen, Jiajun Liang, Zixi Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781665448994

Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its development. Recently, a public dataset ARID has been introduced to stimulate progress for the task of human action recognition in dark videos. Currently, there are multiple models that perform well for action recognition in videos shot under normal illumination. However, research shows that these methods may not be effective in recognizing actions in dark videos. In this paper, we construct a novel neural network architecture: DarkLight Networks, which involves (i) a dual-pathway structure where both dark videos and its brightened counterpart are utilized for effective video representation;and (ii) a self-attention mechanism, which fuses and extracts corresponding and complementary features from the two pathways. Our approach achieves state-of-the-art results on ARID.

关键词： computer vision Fuses Surveillance conferences Neural networks Lighting Feature extraction

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Wasserstein Barycenter for Multi-Source Domain Adaptation

Wasserstein Barycenter for Multi-Source Domain Adaptation

引用

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

作者： Montesuma, Eduardo Fernandes Mboula, Fred Maurice Ngole Univ Fed Ceara Fortaleza Ceara Brazil Univ Paris Saclay Inst LIST CEA F-91120 Palaiseau France

ISBN: (纸本)9781665445092

Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distribution. To overcome the challenges posed by this learning scenario, we propose a method for constructing an intermediate domain between sources and target domain, the Wasserstein Barycenter Transport (WBT). This method relies on the barycenter on Wasserstein spaces for aggregating the source probability distributions. Once the sources have been aggregated, they are transported to the target domain using standard Optimal Transport for Domain Adaptation framework. Additionally, we revisit previous single-source domain adaptation tasks in the context of multi-source scenario. In particular, we apply our algorithm to object and face recognition datasets. Moreover, to diversify the range of applications, we also examine the tasks of music genre recognition and music-speech discrimination. The experiments show that our method has similar performance with the existing state-of-the-art.

关键词： Visualization computer vision Adaptation models Face recognition Probability distribution Data models Acoustics

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BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision

BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...

引用

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

作者： Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

ISBN: (纸本)9781665448994

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.

关键词： Training computer vision Convolution conferences Neural networks Memory management Decoding

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Pose recognition with Cascade Transformers

Pose Recognition with Cascade Transformers

引用

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

作者： Li, Ke Wang, Shijie Zhang, Xiang Xu, Yifan Xu, Weijian Tu, Zhuowen Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665445092

In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

关键词： Heating systems Visualization computer vision Transformers pattern recognition Decoding Task analysis

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Improving Calibration for Long-Tailed recognition

Improving Calibration for Long-Tailed Recognition

引用

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

作者： Zhong, Zhisheng Cui, Jiequan Liu, Shu Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to improve performance. But there is still the vital issue of miscalibration. To address it, we design two methods to improve calibration and performance in such scenarios. Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning. For dataset bias between these two stages due to different samplers, we further propose shifted batch normalization in the decoupling framework. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, Places-LT, and iNaturalist 2018.

关键词： Training Deep learning computer vision Smoothing methods Image recognition Design methodology Benchmark testing

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Image Generators with Conditionally-Independent Pixel Synthesis

Image Generators with Conditionally-Independent Pixel Synthe...

引用

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

作者： Anokhin, I Demochkin, K. Khakhulin, T. Sterkin, G. Lempitsky, V Korzhenkov, D. Samsung AI Ctr Moscow Russia Skolkovo Inst Sci & Technol Moscow Russia

ISBN: (纸本)9781665445092

Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. Here, we present a new architecture for image generators, where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel. No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis. We analyze the modeling capabilities of such generators when trained in an adversarial fashion, and observe the new generators to achieve similar generation quality to state-of-the-art convolutional generators. We also investigate several interesting properties unique to the new architecture.

关键词： computer vision Image color analysis Computational modeling Impedance matching Superresolution computer architecture Rendering (computer graphics)

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Machine Learning-Based Approach for Hand Gesture recognition

Machine Learning-Based Approach for Hand Gesture Recognition

引用

2023 International conference on Disruptive Technologies, ICDT 2023

作者： Deepika, M. Choudhary, Shilpa Kumar, Sandeep Srinivas, Kodi Neil Gogte Institute of Technology Department of Computer Science & Engineering Hyderabad India Koneru Lakshmaiah Educational Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350323887

computer vision and Machine Learning are used to comprehend and extract information from photos and videos. This paper examines how computer vision and machine learning are used to recognize hand gestures, which are the most common form of everyday communication. The proposed method intends to demonstrate how to utilize computer vision and machine learning to identify motions and apply these techniques using Python and OpenCV. These allow us to distinguish hand gestures made in the air. The output, or the computer webcam stream, is displayed on the output screen. © 2023 ieee.

关键词： computer vision

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

引用

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture

MTLSegFormer: Multi-task Learning with Transformers for Sema...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Goncalves, Diogo Nunes Marcato, Jose Zamboni, Pedro Pistori, Hemerson Li, Jonathan Nogueira, Keiller Goncalves, Wesley Nunes Federal University of Mato Grosso Do Sul Faculty of Computer Science Av. Costa e Silva Campo Grande79070-900 MS Brazil Federal University of Mato Grosso Do Sul Faculty of Engineering Architecture and Urbanism and Geography Av. Costa e Silva Campo Grande79070-900 MS Brazil Dom Bosco Catholic University INOVISAO Avenida Tamandaré 6000 Campo Grande79117-900 MS Brazil University of Waterloo Department of Geography and Environmental Management WaterlooONN2L 3G1 Canada University of Stirling Scotland StirlingFK9 4LA United Kingdom

ISBN: (纸本)9798350302493

Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between the branches usually occurs through the concatenation or sum of the feature maps of the branches. However, this type of information exchange does not directly consider the local characteristics of the image nor the level of importance or correlation between the tasks. In this paper, we propose a semantic segmentation method, MTLSegFormer, which combines multi-task learning and attention mechanisms. After the backbone feature extraction, two feature maps are learned for each task. The first map is proposed to learn features related to its task, while the second map is obtained by applying learned visual attention to locally re-weigh the feature maps of the other tasks. In this way, weights are assigned to local regions of the image of other tasks that have greater importance for the specific task. Finally, the two maps are combined and used to solve a task. We tested the performance in two challenging problems with correlated tasks and observed a significant improvement in accuracy, mainly in tasks with high dependence on the others. © 2023 ieee.

关键词： Semantics

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Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing 22

Disentangled Representation with Dual-stage Feature Learning...

引用

22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Wang, Yu-Chun Wang, Chien-Yi Lai, Shang-Hong Natl Tsing Hua Univ Hsinchu Taiwan Microsoft AI R&D Ctr Taipei Taiwan

ISBN: (纸本)9781665409155

As face recognition is widely used in diverse security-critical applications, the study of face anti-spoofing (FAS) has attracted more and more attention. Several FAS methods have achieved promising performance if the attack types in the testing data are included in the training data, while the performance significantly degrades for unseen attack types. It is essential to learn more generalized and discriminative features to prevent overfitting to pre-defined spoof attack types. This paper proposes a novel dual-stage disentangled representation learning method that can efficiently untangle spoof-related features from irrelevant ones. Unlike previous FAS disentanglement works with one-stage architecture, we found that the dual-stage training design can improve the training stability and effectively encode the features to detect unseen attack types. Our experiments show that the proposed method provides superior accuracy than the state-of-the-art methods on several cross-type FAS benchmarks.

关键词： Training Representation learning computer vision Protocols Face recognition Training data computer architecture

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