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AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

AsymmNet: Towards ultralight convolution neural networks usi...

引用

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

作者： Yang, Haojin Shen, Zhen Zhao, Yucheng Hasso Plattner Inst Potsdam Germany Alibaba Cloud Hangzhou Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781665448994

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://***/Spark001/AsymmNet

关键词： Convolutional codes computer vision conferences Computational modeling Neural networks computer architecture pattern recognition

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Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption

Rethinking Ensemble-Distillation for Semantic Segmentation B...

引用

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

作者： Chao, Chen-Hao Cheng, Bo-Wun Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan

ISBN: (纸本)9781665448994

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble. To examine the effectiveness and the robustness of our method, we perform an extensive set of experiments on both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes benchmarks to quantitatively inspect the improvements achievable by our method. We further provide detailed analyses to validate that our design choices are practical and beneficial. The experimental evidence validates that the proposed method indeed offer superior performance, robustness and flexibility in semantic segmentation based UDA tasks against contemporary baseline methods.

关键词： Learning systems computer vision conferences Semantics Benchmark testing Robustness pattern recognition

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Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

Less is More: Pursuing the Visual Turing Test with the Kules...

引用

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

作者： Olague, Gustavo Olague, Matthieu Jacobo-Lopez, Angel R. Ibarra-Vazquez, Gerardo CICESE Res Ctr Ensenada Baja California Mexico Anahuac Univ Queretaro Queretaro Mexico Autonomous Univ San Luis Potosi San Luis Potosi San Luis Potosi Mexico

ISBN: (纸本)9781665448994

The Turing test centers on the idea that if a computer could trick a human into believing that it was human, then the machine was deemed to be intelligent or indistinguishable from people. Designing a visual Turing test involves recognizing objects and their relationships on images and creating a method to derive new concepts from the visual information. Until now, the proposed visual tests heavily use natural language processing to conduct the questionnaire or storytelling. We deviate from the mainstream, and we propose to reframe the visual Turing test through the Kuleshov effect to avoid written or spoken language. The idea resides on elucidating a method that creates the concept of montage synthetically. Like the first days of cinema, we would like to convey messages with the interpretation of image shots that a machine could decipher while comparing it with those scored by humans. The first implementation of this new test uses images from a psychology study where the circumplex model is applied to rate each image. We consider five deep learning methodologies and eight optimizers, and through semiotics, we derive an emotional state in the computer. The results are promising since we confirm that this version of the visual Turing test is challenging as a new research avenue.

关键词： Deep learning Visualization computer vision Image recognition conferences Psychology Motion pictures

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A Unified Model for Line Projections in Catadioptric Cameras with Rotationally Symmetric Mirrors

A Unified Model for Line Projections in Catadioptric Cameras...

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

作者： Miraldo, Pedro Iglesias, Jose Pedro Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Chalmers Univ Technol Gothenburg Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Lines are among the most used computer vision features, in applications such as camera calibration to object detection. Catadioptric cameras with rotationally symmetric mirrors are omnidirectional imaging devices, capturing up to a 360 degrees field of view These are used in many applications ranging from robotics to panoramic vision. Although known for some specific configurations, the modeling of line projection was never fully solved for general central and non-central catadioptric cameras. We start by taking some general point reflection assumptions and derive a line reflection constraint. This constraint is then used to define a line projection into the image. Next, we compare our model with previous methods, showing that our general approach outputs the same polynomial degrees as previous configuration-specific systems. We run several experiments using synthetic and real-world data, validating our line projection model. Lastly, we show an application of our methods to an absolute camera pose problem.

关键词： computer vision Solid modeling Three-dimensional displays Computational modeling Robot vision systems Cameras Reflection

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CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded vision Systems and Applications

CASSOD-Net: Cascaded and Separable Structures of Dilated Con...

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

作者： Chen, Tse-Wei Wang, Deyu Tao, Wei Wen, Dongchao Yin, Lingxiao Ito, Tadayuki Osa, Kinya Kato, Masami Canon Inc Device Technol Dev Headquarters Tokyo Japan Canon Innovat Solut Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665448994

The field of view (FOV) of convolutional neural networks is highly related to the accuracy of inference. Dilated convolutions are known as an effective solution to the problems which require large FOVs. However, for general-purpose hardware or dedicated hardware, it usually takes extra time to handle dilated convolutions compared with standard convolutions. In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently. A CASSOD-Net includes multiple cascaded 2 x 2 dilated filters, which can be used to replace the traditional 3x3 dilated filters without decreasing the accuracy of inference. Two example applications, face detection and image segmentation, are tested with dilated convolutions and the proposed CASSOD modules. The new network for face detection achieves higher accuracy than the previous work with only 47% of filter weights in the dilated convolution layers of the context module. Moreover, the proposed hardware system can accelerate the computations of dilated convolutions, and it is 2.78 times faster than traditional hardware systems when the filter size is 3 x 3.

关键词： Image segmentation Machine vision conferences Hardware pattern recognition Face detection Convolutional neural networks

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Ensemble Spatial and Temporal vision Transformer for Action Units Detection

Ensemble Spatial and Temporal Vision Transformer for Action ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ngoc Tu Vu Van Thong Huynh Trong Nghia Nguyen Soo-Hyung Kim Department of AI Convergence Chonnam National University

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively.

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Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

Generating Adversarial Samples in Mini-Batches May Be Detrim...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Timothy Redgrave Colton Crum Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

Neural networks have been proven to be both highly effective within computer vision, and highly vulnerable to adversarial attacks. Consequently, as the use of neural networks increases due to their unrivaled performance, so too does the threat posed by adversarial attacks. In this work, we build towards addressing the challenge of adversarial robustness by exploring the relationship between the mini-batch size used during adversarial sample generation and the strength of the adversarial samples produced. We demonstrate that an increase in mini-batch size results in a decrease in the efficacy of the samples produced, and we draw connections between these observations and the phenomenon of vanishing gradients. Next, we formulate loss functions such that adversarial sample strength is not degraded by mini-batch size. Our findings highlight a potential risk for underestimating the true (practical) strength of adversarial attacks, and a risk of overestimating a model’s robustness. We share our codes to let others replicate our experiments and to facilitate further exploration of the connections between batch size and adversarial sample strength.

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StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

StillFast: An End-to-End Approach for Short-Term Object Inte...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Francesco Ragusa Giovanni Maria Farinella Antonino Furnari FPV@IPLab University of Catania Next Vision s.r.l. - Spin-off of the University of Catania

Anticipation problem has been studied considering different aspects such as predicting humans’ locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://***/stillfast/.

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EVSRNet: Efficient Video Super-Resolution with Neural Architecture Search

EVSRNet: Efficient Video Super-Resolution with Neural Archit...

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

作者： Liu, Shaoli Zheng, Chengjian Lu, Kaidi Gao, Si Wang, Ning Wang, Bofei Zhang, Diankai Zhang, Xiaofeng Xu, Tianyu ZTE Corp State Key Lab Mobile Network & Mobile Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781665448994

With the development of convolutional neural networks (CNN), the super-resolution results of CNN-based method have far surpassed traditional method. In particular, the CNN-based single image super-resolution method has achieved excellent results. Video sequences contain more abundant information compare with image, but there are few video super-resolution methods that can be applied to mobile devices due to the requirement of heavy computation, which limits the application of video super-resolution. In this work, we propose the Efficient Video Super-Resolution Network (EVSRNet) with neural architecture search for real-time video super-resolution. Extensive experiments show that our method achieves a good balance between quality and efficiency. Finally, we achieve a competitive result of 7.36 where the PSNR is 27.85 dB and the inference time is 11.3 ms/f on the target snapdragon 865 SoC, resulting in a 2nd place in the Mobile AI(MAI) 2021 real-time video super-resolution challenge. It is noteworthy that, our method is the fastest and significantly outperforms other competitors by large margins.

关键词： Visualization Superresolution Video sequences Neural networks computer architecture Streaming media Real-time systems

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Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation

Tree Energy Loss: Towards Sparsely Annotated Semantic Segmen...

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

作者： Liang, Zhiyuan Wang, Tiancai Zhang, Xiangyu Sun, Jian Shen, Jianbing Beijing Inst Technol Beijing Peoples R China MEGVII Technol Beijing Peoples R China Univ Macau SKL IOTSC Zhuhai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e., point-, scribble-, and block-wise) supervisions, where only a small proportion of pixels are labeled in each image. In this paper, we propose a novel tree energy loss for SASS by providing semantic guidance for unlabeled pixels. The tree energy loss represents images as minimum spanning trees to model both low-level and high-level pair-wise affinities. By sequentially applying these affinities to the net-work prediction, soft pseudo labels for unlabeled pixels are generated in a coarse-to-fine manner, achieving dynamic online self-training. The tree energy loss is effective and easy to be incorporated into existing frameworks by combining it with a traditional segmentation loss. Compared with previous SASS methods, our method requires no multi-stage training strategies, alternating optimization procedures, additional supervised data, or time-consuming post-processing while outperforming them in all SASS settings. Code is available at https://***/megvii-research/TreeEnergyLoss.

关键词： Training Energy loss Image segmentation computer vision Shape Computational modeling Semantics

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