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Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation

Alpha-Refine: Boosting Tracking Performance by Precise Bound...

引用

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

作者： Yan, Bin Zhang, Xinyu Wang, Dong Lu, Huchuan Yang, Xiaoyun Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Remark AI London England

ISBN: (纸本)9781665445092

Visual object tracking aims to precisely estimate the bounding box for the given target, which is a challenging problem due to factors such as deformation and occlusion. Many recent trackers adopt the multiple-stage strategy to improve bounding box estimation. These methods first coarsely locate the target and then refine the initial prediction in the following stages. However, existing approaches still suffer from limited precision, and the coupling of different stages severely restricts the method's transferability. This work proposes a novel, flexible, and accurate refinement module called Alpha-Refine (AR), which can significantly improve the base trackers' box estimation quality. By exploring a series of design options, we conclude that the key to successful refinement is extracting and maintaining detailed spatial information as much as possible. Following this principle, Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an auxiliary mask head as the core components. Comprehensive experiments on TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers show that our approach significantly improves the base tracker's performance with little extra latency. The proposed Alpha-Refine method leads to a series of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp) and the ARDiMP50 (AR strengthened DiMP50) achieve good efficiency-precision trade-off while the ARDiMPsuper (AR strengthened DiMPsuper) achieves very competitive performance at a realtime speed. Code and pretrained models are available at hups://***/MasterBin-IIALI/Alphalteline.

关键词： Couplings Visualization computer vision Target tracking Correlation Computational modeling Estimation

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Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

Efficient Space-time Video Super Resolution using Low-Resolu...

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

作者： Dutta, Saikat Shah, Nisarg A. Mittal, Anurag IIT Madras Chennai Tamil Nadu India IIT Jodhpur Karwar Rajasthan India

ISBN: (纸本)9781665448994

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A simplistic solution is the sequential running of Video Super Resolution and Video Frame interpolation models. However, this type of solutions are memory inefficient, have high inference time, and could not make the proper use of space-time relation property. To this extent, we first interpolate in LR space using quadratic modeling. Input LR frames are super-resolved using a state-of-the-art Video Super-Resolution method. Flowmaps and blending mask which are used to synthesize LR interpolated frame is reused in HR space using bilinear upsampling. This leads to a coarse estimate of HR intermediate frame which often contains artifacts along motion boundaries. We use a refinement network to improve the quality of HR intermediate frame via residual learning. Our model is lightweight and performs better than current state-of-the-art models in REDS STSR Validation set.

关键词： Interpolation computer vision conferences Superresolution Refining pattern recognition Videos

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Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution

Learning Scene Structure Guidance via Cross-Task Knowledge T...

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

作者： Sun, Baoli Ye, Xinchen Li, Baopu Li, Haojie Wang, Zhihui Xu, Rui Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training samples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected in actual testing environment. Therefore, we explore for the first time to learn the cross-modality knowledge at training stage, where both RGB and depth modalities are available, but test on the target dataset, where only single depth modality exists. Our key idea is to distill the knowledge of scene structural guidance from RGB modality to the single DSR task without changing its network architecture. Specifically, we construct an auxiliary depth estimation (DE) task that takes an RGB image as input to estimate a depth map, and train both DSR task and DE task collaboratively to boost the performance of DSR. Upon this, a cross-task interaction module is proposed to realize bilateral cross-task knowledge transfer. First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion. Then, we advance a structure prediction (SP) task that provides extra structure regularization to help both DSR and DE networks learn more informative structure representations for depth recovery. Extensive experiments demonstrate that our scheme achieves superior performance in comparison with other DSR methods.

关键词： Training Knowledge engineering Runtime Superresolution Network architecture pattern recognition Task analysis

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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

ORDisCo: Effective and Efficient Usage of Incremental Unlabe...

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

作者： Wang, Liyuan Yang, Kuo Li, Chongxuan Hong, Lanqing Li, Zhenguo Zhu, Jun Tsinghua Univ BNRist Ctr Inst AI Dept Comp Sci & TechTHBI Lab Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications. In this work, we consider semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data. Observing that existing continual learning methods lack the ability to continually exploit the unlabeled data, we propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier. In particular, ORDisCo replays data sampled from the conditional generator to the classifier in an online manner, exploiting unlabeled data in a time- and storage-efficient way. Further, to explicitly overcome the catastrophic forgetting of unlabeled data, we selectively stabilize parameters of the discriminator that are important for discriminating the pairs of old unlabeled data and their pseudo-labels predicted by the classifier. We extensively evaluate ORDisCo on various semi-supervised learning benchmark datasets for SSCL, and show that ORDisCo achieves significant performance improvement on SVHN, CIFAR10 and Tiny-ImageNet, compared to strong baselines.

关键词： Deep learning computer vision Systematics Semisupervised learning Benchmark testing Generative adversarial networks Generators

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Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

Adaptive Consistency Regularization for Semi-Supervised Tran...

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

作者： Abuduweili, Abulikemu Li, Xingjian Shi, Humphrey Xu, Cheng-Zhong Dou, Dejing Baidu Res Big Data Lab Beijing Peoples R China Univ Oregon SHI Lab Eugene OR 97403 USA Univ Macau Dept Comp Sci State Key Lab IOTSC Taipa Macao Peoples R China

ISBN: (纸本)9781665445092

While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is randomly initialized. In this work, we consider semi-supervised learning and transfer learning jointly, leading to a more practical and competitive paradigm that can utilize both powerful pre-trained models from source domain as well as labeled/unlabeled data in the target domain. To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples. Examples involved in the consistency regularization are adaptively selected according to their potential contributions to the target task. We conduct extensive experiments on popular benchmarks including CIFAR-10, CUB-200, and MURA, by fine-tuning the ImageNet pre-trained ResNet-50 model. Results show that our proposed adaptive consistency regularization outperforms state-of-the-art semi-supervised learning techniques such as Pseudo Label, Mean Teacher, and FixMatch. Moreover, our algorithm is orthogonal to existing methods and thus able to gain additional improvements on top of MixMatch and Fix Match. Our code is available at https://***/Walleclipse/SemiSupervised-Transfer-Learning-Paddle.

关键词： Adaptation models computer vision Codes Transfer learning Supervised learning Semisupervised learning Benchmark testing

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Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing

Perception Matters: Detecting Perception Failures of VQA Mod...

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

作者： Yuan, Yuanyuan Wang, Shuai Jiang, Mingyue Chen, Tsong Yueh HKUST Hong Kong Peoples R China Zhejiang Sci Tech Univ Hangzhou Peoples R China Swinburne Univ Technol Hawthorn Vic Australia

ISBN: (纸本)9781665445092

Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning questions. Nevertheless, Given that perception tasks (e.g., recognizing objects) are the building blocks in the compositional process required by high-level reasoning, there is a demanding need to gain insights into how much of a problem lowlevel perception is. Inspired by the principles of software metamorphic testing, we introduce MetaVQA, a model-agnostic framework for benchmarking perception capability of VQA models. Given an image i, MetaVQA is able to synthesize a low-level perception question q. It then jointly transforms (i, q) to one or a set of sub-questions and subimages. MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images. Violating MRs denotes a failure of answering perception questions. MetaVQA successfully detects over 4.9 million perception failures made by popular VQA models with metamorphic testing. The state-of-the-art VQA models (e.g., the champion of VQA 2020 Challenge) suffer from perception consistency problems. In contrast, the Oscar VQA models, by using anchor points to align questions and images, show generally better consistency in perception tasks. We hope MetaVQA will revitalize interest in enhancing the low-level perceptual abilities of VQA models, a cornerstone of high-level reasoning.

关键词： Visualization computer vision Computational modeling Transforms Benchmark testing Knowledge discovery Cognition

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Visual recognition by Request

Visual Recognition by Request

引用

conference on computer vision and pattern recognition (cvpr)

作者： Chufeng Tang Lingxi Xie Xiaopeng Zhang Xiaolin Hu Qi Tian Department of Computer Science and Technology Tsinghua University Huawei Inc. Chinese Institute for Brain Research (CIBR) IDG/McGovern Institute for Brain Research THBI Tsinghua University

Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establish a new paradigm named visual recognition by request (ViRReq 1 1 We recommend the readers to pronounce ViRReqas/virik/.) to bridge the gap. The key lies in decomposing visual recognition into atomic tasks named requests and leveraging a knowledge base, a hierarchical and text-based dictionary, to assist task definition. ViRReq allows for (i) learning complicated whole-part hierarchies from highly incomplete annotations and (ii) inserting new concepts with minimal efforts. We also establish a solid baseline by integrating language-driven recognition into recent semantic and instance segmentation methods, and demonstrate its flexible recognition ability on CPP and ADE20K, two datasets with hierarchical whole-part annotations.

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Total Selfie: Generating Full-Body Selfies

Total Selfie: Generating Full-Body Selfies

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bowei Chen Brian Curless Ira Kemelmacher-Shlizerman Steven M. Seitz University of Washington

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a method to generate full-body selfies from photographs originally taken at arms length. Because self-captured photos are typically taken close up, they have lim-ited field of view and exaggerated perspective that distorts facial shapes. We instead seek to generate the photo some one else would take of you from a few feet away. Our approach takes as input four selfies of your face and body, a background image, and generates a full-body selfie in a de-sired target pose. We introduce a novel diffusion-based approach to combine all of this information into high-quality, well-composed photos of you with the desired pose and background.

关键词： computer vision Shape Face recognition

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Improved Noise2Noise Denoising with Limited Data

Improved Noise2Noise Denoising with Limited Data

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

作者： Calvarons, Adria Font Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Deep learning methods have proven to be very effective for the task of image denoising even when clean reference images are not available. In particular, Noise2Noise, which requires pairs of noisy images during the training phase, has been shown to yield results as good as approaches using pairs of noisy and clean images (Noise2Clean). However, the performance of Noise2Noise drops when the amount of training data is reduced, limiting its capability in practical scenarios. In this work, an analysis of the Noise2Noise learning strategy is done using real noise and synthetic datasets. This paper demonstrates, using diverse network architectures and loss functions, that the duplicity of information in the noisy pairs can be exploited to reach increased denoising performance of Noise2Noise. Additionally, the issue of overfitting in Noise2Noise is analyzed, given its relevance when training with limited data, and an interpretable early termination criterion is proposed.

关键词： Training Limiting Image color analysis Noise reduction Training data Network architecture pattern recognition

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Adaptive Spatial-Temporal Fusion of Multi-Objective Networks for Compressed Video Perceptual Enhancement

Adaptive Spatial-Temporal Fusion of Multi-Objective Networks...

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

作者： Zheng, He Li, Xin Liu, Fanglong Jiang, Lielin Zhang, Qi Li, Fu Dang, Qingqing He, Dongliang Baidu Inc Dept Comp Vis Technol VIS Bldg 2Baidu Sci Pk Beijing Peoples R China

ISBN: (纸本)9781665448994

Perceptual quality enhancement of heavily compressed videos is a difficult, unsolved problem because there still not exists a suitable perceptual similarity loss function between two video pairs. Motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously, in this paper, we propose a simple yet effective novel solution dubbed "Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks" (ASTF) to adaptive fuse the enhancement results from networks trained with two different optimization objectives. Specifically, the proposed ASTF takes an enhancement frame along with its neighboring frames as input to jointly predict a mask to indicate regions with high-frequency textual details. Then we use the mask to fuse two enhancement results which can retain both smooth content and rich textures. Extensive experiments show that our method achieves a promising performance of compressed video perceptual quality enhancement.

关键词： Training computer vision Adaptation models Adaptive systems Fuses conferences pattern recognition

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