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Masked Jigsaw Puzzle: A Versatile Position Embedding for vision Transformers

Masked Jigsaw Puzzle: A Versatile Position Embedding for Vis...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bin Ren Yahui Liu Yue Song Wei Bi Rita Cucchiara Nicu Sebe Wei Wang University of Pisa Italy University of Trento Italy Tencent AI Lab China University of Modena and Reggio Emilia Italy Beijing Jiaotong University China

Position Embeddings (PEs), an arguably indispensable component in vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about the impact of PEs on accuracy, privacy, prediction consistency, etc. To tackle these issues, we propose a Masked Jigsaw Puzzle (MJP) position embedding method. In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded. Meanwhile, for the non-occluded patches, the PEs remain the original ones but their spatial relation is strengthened via our dense absolute localization regressor. The experimental results reveal that 1) PEs explicitly encode the 2D spatial relationship and lead to severe privacy leakage problems under gradient inversion attack; 2) Training ViTs with the naively shuffled patches can alleviate the problem, but it harms the accuracy; 3) Under a certain shuffle ratio, the proposed MJP not only boosts the performance and robustness on large-scale datasets (i.e., ImageNet-1K and ImageNet-C, -A/O) but also improves the privacy preservation ability under typical gradient attacks by a large margin. The source code and trained models are available at https://***/yhlleo/MJP.

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Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Multi-Class Multi-Movement Vehicle Counting Based on CenterT...

引用

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

作者： Kocur, Viktor Ftacnik, Milan Comenius Univ Fac Math Phys & Informat Bratislava Slovakia

ISBN: (纸本)9781665448994

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

关键词： computer vision Tracking conferences Urban areas Neural networks Object detection computer architecture

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Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

Joint Video Multi-Frame Interpolation and Deblurring under U...

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conference on computer vision and pattern recognition (cvpr)

作者： Wei Shang Dongwei Ren Yi Yang Hongzhi Zhang Kede Ma Wangmeng Zuo School of Computer Science and Technology Harbin Institute of Technology City University of Hong Kong Peng Cheng Laboratory Shenzhen

Natural videos captured by consumer cameras often suffer from low framerate and motion blur due to the combination of dynamic scene complexity, lens and sensor imperfection, and less than ideal exposure setting. As a result, computational methods that jointly perform video frame interpolation and deblurring begin to emerge with the unrealistic assumption that the exposure time is known and fixed. In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame interpolation and deblurring under unknown exposure time. Toward this goal, we first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then train two U-Nets for intramotion and inter-motion analysis, respectively, adapting to the learned exposure representation via gain tuning. We finally build our video reconstruction network upon the exposure and motion representation by progressive exposureadaptive convolution and motion refinement. Extensive experiments on both simulated and real-world datasets show that our optimized method achieves notable performance gains over the state-of-the-art on the joint video ×8 interpolation and deblurring task. Moreover, on the seemingly implausible ×16 interpolation task, our method outperforms existing methods by more than 1.5 dB in terms of PSNR.

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Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery

Bootstrap Your Own Prior: Towards Distribution-Agnostic Nove...

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conference on computer vision and pattern recognition (cvpr)

作者： Muli Yang Liancheng Wang Cheng Deng Hanwang Zhang School of Electronic Engineering Xidian University Xi'an China School of Computer Science and Engineering Nanyang Technological University Singapore

Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes. Existing works hold an impractical assumption that the novel class distribution prior is uniform, yet neglect the imbalanced nature of real-world data. In this paper, we relax this assumption by proposing a new challenging task: distribution-agnostic NCD, which allows data drawn from arbitrary unknown class distributions and thus renders existing methods useless or even harmful. We tackle this challenge by proposing a new method, dubbed “Boot-strapping Your Own Prior (BYOP)”, which iteratively estimates the class prior based on the model prediction it-self. At each iteration, we devise a dynamic temperature technique that better estimates the class prior by encouraging sharper predictions for less-confident samples. Thus, BYOP obtains more accurate pseudo-labels for the novel samples, which are beneficial for the next training iteration. Extensive experiments show that existing methods suffer from imbalanced class distributions, while BYOp 1 1 Code: https://***/muliyangm/BYOP. out-performs them by clear margins, demonstrating its effectiveness across various distribution scenarios.

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Probability-based Global Cross-modal Upsampling for Pansharpening

Probability-based Global Cross-modal Upsampling for Pansharp...

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conference on computer vision and pattern recognition (cvpr)

作者： Zeyu Zhu Xiangyong Cao Man Zhou Junhao Huang Deyu Meng Xi'an Jiaotong University School of Computer Science and Technology Xi'an Jiaotong University Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University Nanyang Technological University Macau University of Science and Technology

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://***/Zeyu-Zhu/PGCU.

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Activate or Not: Learning Customized Activation

Activate or Not: Learning Customized Activation

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

作者： Ma, Ningning Zhang, Xiangyu Liu, Ming Sun, Jian Hong Kong Univ Sci & Technol Hong Kong Peoples R China MEGVII Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searched activation, can be interpreted as a smooth approximation to ReLU. Intuitively, in the same way, we approximate the more general Maxout family to our novel ACON family, which remarkably improves the performance and makes Swish a special case of ACON. Next, we present meta-ACON, which explicitly learns to optimize the parameter switching between non-linear (activate) and linear (inactivate) and provides a new design space. By simply changing the activation function, we show its effectiveness on both small models and highly optimized large models (e.g. it. improves the ImageNet top-1 accuracy rate by 6.7% and 1.8% on MobileNet0.25 and ResNet-152, respectively). Moreover, our novel ACON can be naturally transferred to object detection and semantic segmentation, showing that ACON is an effective alternative in a variety of tasks. Code is available at https: // ***/nmaac/acon.

关键词： Image segmentation computer vision Codes Semantics Neurons Switches Object detection

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Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations

Three Birds with One Stone: Multi-Task Temporal Action Detec...

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

作者： Li, Zhihui Yao, Lina Univ New South Wales Sch Comp Sci & Engn Kensington NSW Australia

ISBN: (纸本)9781665445092

Temporal action detection on unconstrained videos has seen significant research progress in recent years. Deep learning has achieved enormous success in this direction. However, collecting large-scale temporal detection datasets to ensuring promising performance in the real-world is a laborious, impractical and time consuming process. Accordingly, we present a novel improved temporal action localization model that is better able to take advantage of limited labeled data available. Specifically, we design two auxiliary tasks by reconstructing the available label information and then facilitate the learning of the temporal action detection model. Each task generates their supervision signal by recycling the original annotations, and are jointly trained with the temporal action detection model in a multitask learning fashion. Note that the proposed approach can be pluggable to any region proposal based temporal action detection models. We conduct extensive experiments on three benchmark datasets, namely THUMOS' 14 [15], Charades [35] and ActivityNet [14]. Our experimental results confirm the effectiveness of the proposed model.

关键词： Location awareness Annotations Estimation Object segmentation Predictive models Recycling pattern recognition

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StereoPIFu: Depth Aware Clothed Human Digitization via Stereo vision

StereoPIFu: Depth Aware Clothed Human Digitization via Stere...

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

作者： Hong, Yang Zhang, Juyong Jiang, Boyi Guo, Yudong Liu, Ligang Bao, Hujun Univ Sci & Technol China Hefei Anhui Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based network to enable depth-aware reconstruction. Moreover, the novel relative z-offset is employed to associate predicted high-fidelity human depth and occupancy inference, which helps restore fine-level surface details. Second, a network structure that fully utilizes the geometry information from the stereo images is designed to improve the human body reconstruction quality. Consequently, our StereoPIFu can naturally infer the human body's spatial location in camera space and maintain the correct relative position of different parts of the human body, which enables our method to capture human performance. Compared with previous works, our StereoPIFu significantly improves the robustness, completeness, and accuracy of the clothed human reconstruction, which is demonstrated by extensive experimental results.

关键词： Geometry Surface reconstruction computer vision Three-dimensional displays Shape Robustness Stereo vision

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Gradient-based Algorithms for Machine Teaching

Gradient-based Algorithms for Machine Teaching

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

作者： Wang, Pei Nagrecha, Kabir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9781665445092

The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing platforms, who tend to perform at least as well as machine learning systems. It is shown that, if allowed unbounded effort, the optimal student always learns the optimal predictor for a classification task. Hence, the role of the optimal teacher is to select the teaching set that minimizes student effort. This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population. The optimal teacher, denoted MaxGrad, is then shown to maximize the gradient of the risk on the set of new examples selected per iteration. MaxGrad teaching algorithms are finally provided for both binary and multiclass tasks, and shown to have some similarities with boosting algorithms. Experimental evaluations demonstrate the effectiveness of MaxGrad, which outperforms previous algorithms on the classification task, for both machine learning and human students from MTurk, by a substantial margin.

关键词： Training Machine learning algorithms Education Sociology Classification algorithms pattern recognition Risk management

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ContactOpt: Optimizing Contact to Improve Grasps

ContactOpt: Optimizing Contact to Improve Grasps

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

作者： Grady, Patrick Tang, Chengcheng Twigg, Christopher D. Minh Vo Brahmbhatt, Samarth Kemp, Charles C. Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Redmond WA USA Intel Labs Hillsboro OR USA

ISBN: (纸本)9781665445092

Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differentiable contact model. Notably, our contact model encourages mesh interpenetration to approximate deformable soft tissue in the hand. In our evaluations, our methods result in grasps that better match ground truth contact, have lower kinematic error, and are significantly preferred by human participants. Code and models are available online(1).

关键词： Deformable models computer vision Codes Pose estimation Biological tissues Kinematics Data models

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