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GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency

GradICON: Approximate Diffeomorphisms via Gradient Inverse C...

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

作者： Tian, Lin Greer, Hastings Vialard, Francois-Xavier Kwitt, Roland Estepar, Raul San Jose Rushmore, Richard Jarrett Makris, Nikolaos Bouix, Sylvain Niethammer, Marc Univ N Carolina Marlborough MA 01752 USA Univ Gustave Eiffel LIGM Marne La Vallee France INRIA Paris MOKAPLAN Paris France Univ Salzburg Salzburg Austria Harvard Med Sch Boston MA USA Boston Univ Boston MA USA ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350301298

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer - GradICON results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. Code is available at https://***/uncbiag/ICON.

关键词： cell microscopy Medical and biological vision

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Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris recognition Systems

Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Banerjee, Sudipta Jain, Anubhav Jiang, Zehua Memon, Nasir Togelius, Julian Ross, Arun NYU New York NY 10003 USA Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350370287;9798350370713

A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition systems. We present an hitherto unknown vulnerability wherein we mix IrisCodes using simple bitwise operators to generate alpha-mixtures -alpha-wolves (combining a set of "wolf" samples) and alpha-mammals (combining a set of users selected via search optimization) that increase false matches. We evaluate this vulnerability using the IITD, CASIA-IrisV4-Thousand and Synthetic datasets, and observe that an alpha-wolf (from two wolves) can match upto 71 identities @FMR=0.001%, while an alpha-mammal (from two identities) can match upto 133 other identities @FMR=0.01% on the IITD dataset.

关键词： Mammals

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Label-Free Synthetic Pretraining of Object Detectors

Label-Free Synthetic Pretraining of Object Detectors

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Law, Hei Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350318920;9798350318937

We propose a new approach, Synthetic Optimized Layout with Instance Detection (SOLID), to pretrain object detectors with synthetic images. Our "SOLID" approach consists of two main components: (1) generating synthetic images using a collection of unlabelled 3D models with optimized scene arrangement;(2) pretraining an object detector on "instance detection" task-given a query image depicting an object, detecting all instances of the exact same object in a target image. Our approach does not need any semantic labels for pretraining and allows the use of arbitrary, diverse 3D models. Experiments on COCO show that with optimized data generation and a proper pretraining task, synthetic data can be highly effective data for pretraining object detectors. In particular, pretraining on rendered images achieves performance competitive with pretraining on real images while using significantly less computing resources. Code is available at https://***/princeton-vl/SOLID.

关键词： Algorithms Image recognition and understanding

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Temporally-Consistent Video Semantic Segmentation with Bidirectional Occlusion-guided Feature Propagation

Temporally-Consistent Video Semantic Segmentation with Bidir...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Baghbaderani, Razieh Kaviani Li, Yuanxin Wang, Shuangquan Qi, Hairong Samsung Semicond Inc SOC R&D San Jose CA 95134 USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9798350318920;9798350318937

Despite recent progress in static image segmentation, video segmentation is still challenging due to the need for an accurate, fast, and temporally consistent model. Conducting per-frame static image segmentation on a video is not acceptable since it is computationally prohibitive and prone to temporal inconsistency. In this paper, we present bidirectional occlusion-guided feature propagation (BOFP) method with the goal of improving temporal consistency of segmentation results without sacrificing segmentation accuracy, while at the same time keeping the operations at a low computation cost. It leverages temporal coherence in the video by feature propagation from keyframes to other frames along the motion paths in both forward and backward directions. We propose an occlusion-based attention network to estimate the distorted areas based on bidirectional optical flows, and utilize them as cues for correcting and fusing the propagated features. Extensive experiments on benchmark datasets demonstrate that the proposed BOFP method achieves superior performance in terms of temporal consistency while maintaining comparable level of segmentation accuracy at a low computation cost, striking a great balance among the three performance metrics essential to evaluate video segmentation solutions.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Optical Flow Domain Adaptation via Target Style Transfer

Optical Flow Domain Adaptation via Target Style Transfer

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yoon, Jeongbeen Kim, Sanghyun Kwak, Suha Cho, Minsu Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (纸本)9798350318920;9798350318937

Optical flows play an integral role for a variety of motion-related tasks such as action recognition, object segmentation, and tracking in videos. While state-of-the-art optical flow methods heavily rely on learning, the learned optical flow methods significantly degrade when applied to different domains, and the training datasets are very limited due to the extreme cost of flow-level annotation. To tackle the issue, we introduce a domain adaptation technique for optical flow estimation. Our method extracts diverse style statistics of the target domain and use them in training to generate synthetic features from the source features, which contain the contents of the source but the style of the target. We also impose motion consistency between the synthetic target and the source and deploy adversarial learning at the flow prediction to encourage domain-invariant features. Experimental results show that the proposed method achieves substantial and consistent improvements in different domain adaptation scenarios on VKITTI 2, Sintel, and KITTI 2015 benchmarks.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Knowledge-Distillation-Based Label Smoothing for Fine-Grained Open-Set Vehicle recognition

Knowledge-Distillation-Based Label Smoothing for Fine-Graine...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wolf, Stefan Loran, Dennis Beyerer, Juergen Karlsruhe Inst Technol Vis & Fus Lab IES Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning Munich Germany

ISBN: (纸本)9798350370287;9798350370713

Fine-grained vehicle classification describes the task of estimating the make and the model of a vehicle based on an image. It provides a useful tool for security authorities to find suspects in surveillance cameras. However, most research about fine-grained vehicle classification is only focused on a closed-set scenario which considers all possible classes to be included in the training. This is not realistic for real-world surveillance applications where the images fed into the classifier can be of arbitrary vehicle models and the large number of commercially available vehicle models renders learning all models impossible. Thus, we investigate fine-grained vehicle classification in an open-set recognition scenario which includes unknown vehicle models in the test set and expects these samples to be rejected. Our experiments highlight the importance of label smoothing for open-set recognition performance. Nonetheless, it lacks recognizing the different semantic distances between vehicle models which result in largely different confusion probabilities. Thus, we propose a knowledge-distillation-based label smoothing approach which considers these different semantic similarities and thus, improves the closedset classification as well as the open-set recognition performance.

关键词： Semantics

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Curricular Contrastive Regularization for Physics-aware Single Image Dehazing

Curricular Contrastive Regularization for Physics-aware Sing...

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

作者： Zheng, Yu Zhan, Jiahui He, Shengfeng Dong, Junyu Du, Yong Ocean Univ China Coll Comp Sci & Technol Qingdao Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore Singapore

ISBN: (纸本)9798350301298

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically im-balanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C(2)PNet. Extensive experiments demonstrate that our C(2)PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. Code is available at https://***/YuZheng9/C2PNet.

关键词： Low-level vision

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Distilling Cross-Temporal Contexts for Continuous Sign Language recognition

Distilling Cross-Temporal Contexts for Continuous Sign Langu...

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

作者： Guo, Leming Xue, Wanli Guo, Qing Liu, Bo Zhang, Kaihua Yuan, Tiantian Chen, Shengyong Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China ASTAR Ctr Frontier AI Res Singapore Singapore Walmart Global Tech Sunnyvale CA USA Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Tianjin Univ Technol Tech Coll Deaf Tianjin Peoples R China

ISBN: (纸本)9798350301298

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thorough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distillation learning objective to aggregate the two types of context and the linguistic prior. The knowledge distillation enables the resultant one-branch temporal aggregation module to perceive local-global temporal and semantic context. This shallow temporal perception module structure facilitates spatial perception module learning. Extensive experiments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

关键词： body gesture Humans: Face movement pose

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Improving Selective Visual Question Answering by Learning from Your Peers

Improving Selective Visual Question Answering by Learning fr...

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

作者： Dancette, Corentin Whitehead, Spencer Maheshwary, Rishabh Vedantam, Ramakrishna Scherer, Stefan Chen, Xinlei Cord, Matthieu Rohrbach, Marcus Meta AI FAIR New York NY 10013 USA Meta Real Labs Res New York NY USA Sorbonne Univ Paris France Valeo Ai Paris France

ISBN: (纸本)9798350301298

ability of models to assess their own correctness remains under-explored. Recent work has shown that VQA models, out-of-the-box, can have difficulties abstaining from answering when they are wrong. The option to abstain, also called Selective Prediction, is highly relevant when deploying systems to users who must trust the system's output (e.g., VQA assistants for users with visual impairments). For such scenarios, abstention can be especially important as users may provide out-of-distribution (OOD) or adversarial inputs that make incorrect answers more likely. In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data. The goal is to maximize the number of questions answered while minimizing the risk of error on those questions. We propose a simple yet effective Learning from Your Peers (LYP) approach for training multimodal selection functions for making abstention decisions. Our approach uses predictions from models trained on distinct subsets of the training data as targets for optimizing a Selective VQA model. It does not require additional manual labels or held-out data and provides a signal for identifying examples that are easy/difficult to generalize to. In our extensive evaluations, we show this benefits a number of models across different architectures and scales. Overall, for ID, we reach 32.92% in the selective prediction metric coverage at 1% risk of error (C@1%) which doubles the previous best coverage of 15.79% on this task. For mixed ID/OOD, using models' softmax confidences for abstention decisions performs very poorly, answering <5% of questions at 1% risk of error even when faced with only 10% OOD examples, but a learned selection function with LYP can increase that to 25.38% C@1%.

关键词： and reasoning language vision

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Elusive Images: Beyond Coarse Analysis for Fine-Grained recognition

Elusive Images: Beyond Coarse Analysis for Fine-Grained Reco...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Anderson, Connor Gwilliam, Matt Gaskin, Evelyn Farrell, Ryan Brigham Young Univ Provo UT 84602 USA Univ Maryland College Pk MD USA

ISBN: (纸本)9798350318920;9798350318937

While the community has seen many advances in recent years to address the challenging problem of Fine-grained Visual Categorization (FGVC), progress seems to be slowing-new state-of-the-art methods often distinguish themselves by improving top-1 accuracy by mere tenths of a percent. However, across all of the now-standard FGVC datasets, there remain sizeable portions of the test data that none of the current state-of-the-art (SOTA) models can successfully predict. This paper provides a framework for identifying and studying the errors that current methods make across diverse fine-grained datasets. Three models of difficulty-Prediction Overlap, Prediction Rank and Pairwise Class Confusion-are employed to highlight the most challenging sets of images and classes. Extensive experiments apply a range of standard and SOTA methods, evaluating them on multiple FGVC domains and datasets. Insights acquired from coupling these difficulty paradigms with the careful analysis of experimental results suggest crucial areas for future FGVC research, focusing critically on the set of elusive images that none of the current models can correctly classify. Code is available at ***/elusive-images-fgvc.

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

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