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MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

MetaAlign: Coordinating Domain Alignment and Classification ...

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

作者： Wei, Guoqiang Lan, Cuiling Zeng, Wenjun Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics. However, the optimization objective of such domain alignment is generally not coordinated with that of the object classification task itself such that their descent directions for optimization may be inconsistent. This will reduce the effectiveness of domain alignment in improving the performance of UDA. In this paper, we aim to study and alleviate the optimization inconsistency problem between the domain alignment and classification tasks. We address this by proposing an effective meta-optimization based strategy dubbed MetaAlign, where we treat the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme. MetaAlign encourages both tasks to be optimized in a coordinated way, which maximizes the inner product of the gradients of the two tasks during training. Experimental results demonstrate the effectiveness of our proposed method on top of various alignment-based baseline approaches, for tasks of object classification and object detection. MetaAlign helps achieve the state-of-the-art performance.

关键词： Training computer vision Object detection Adversarial machine learning pattern recognition Task analysis Optimization

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FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions

FCPose: Fully Convolutional Multi-Person Pose Estimation wit...

引用

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

作者： Mao, Weian Tian, Zhi Wang, Xinlong Shen, Chunhua Univ Adelaide Adelaide SA Australia Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping post-processing with dynamic instance-aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. Moreover, with the strong representation capacity of dynamic convolutions, the keypoint heads in FCPose are designed to be very compact, resulting in fast inference and making FCPose have almost constant inference time regardless of the number of persons in the image. For example, on the COCO dataset, a real-time version of FCPose using the DLA-34 backbone infers about 4.5xfaster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26 FPS) while achieving improved performance (64.8% AP kp vs. 64.3% AP kp ). FCPose also offers better speed/accuracy trade-off than other state-of-the-art methods. Our experiment results show that FCPose is a simple yet effective multi-person pose estimation framework.

关键词： Convolutional codes computer vision Head Pose estimation Graphics processing units Real-time systems pattern recognition

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NeX: Real-time View Synthesis with Neural Basis Expansion

NeX: Real-time View Synthesis with Neural Basis Expansion

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

作者： Wizadwongsa, Suttisak Phongthawee, Pakkapon Yenphraphai, Jiraphon Suwajanakorn, Supasorn VISTEC Rayong Thailand

ISBN: (纸本)9781665445092

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects-in real time. Unlike traditional MP! that uses a set of simple RGBcx planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forward-facing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as the rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000x faster rendering time than the state of the art. For real-time demos, visit https://***/

关键词： Reflectivity Measurement Neural networks Rendering (computer graphics) Real-time systems Reflection Light fields

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Hard Patches Mining for Masked Image Modeling

Hard Patches Mining for Masked Image Modeling

引用

conference on computer vision and pattern recognition (cvpr)

作者： Haochen Wang Kaiyou Song Junsong Fan Yuxi Wang Jin Xie Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Megvii Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Science

Masked image modeling (MIM) has attracted much research attention due to its promising potential for learning scalable visual representations. In typical approaches, models usually focus on predicting specific contents of masked patches, and their performances are highly related to pre-defined mask strategies. Intuitively, this procedure can be considered as training a student (the model) on solving given problems (predict masked patches). However, we argue that the model should not only focus on solving given problems, but also stand in the shoes of a teacher to produce a more challenging problem by itself. To this end, we propose Hard Patches Mining (HPM), a brand-new framework for MIM pre-training. We observe that the reconstruction loss can naturally be the metric of the difficulty of the pretraining task. Therefore, we introduce an auxiliary loss predictor, predicting patch-wise losses first and deciding where to mask next. It adopts a relative relationship learning strategy to prevent overfitting to exact reconstruction loss values. Experiments under various settings demonstrate the effectiveness of HPM in constructing masked images. Furthermore, we empirically find that solely introducing the loss prediction objective leads to powerful representations, verifying the efficacy of the ability to be aware of where is hard to reconstruct. 1 1 Code: https://***/Haochen-wang409/HPM

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Improving the Transferability of Adversarial Samples by Path-Augmented Method

Improving the Transferability of Adversarial Samples by Path...

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conference on computer vision and pattern recognition (cvpr)

作者： Jianping Zhang Jen-tse Huang Wenxuan Wang Yichen Li Weibin Wu Xiaosen Wang Yuxin Su Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong School of Software Engineering Sun Yat-sen University Huawei Singular Security Lab Beijing China

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates.

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Information Bottleneck Disentanglement for Identity Swapping

Information Bottleneck Disentanglement for Identity Swapping

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

作者： Gao, Gege Huang, Huaibo Fu, Chaoyou Li, Zhaoyang He, Ran Univ Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Sch Artificial Intelligence Natl Lab Pattern RecognitCASIACAS Beijing Peoples R China

ISBN: (纸本)9781665445092

Improving the performance of face forgery detectors often requires more identity-swapped images of higher-quality. One core objective of identity swapping is to generate identity-discriminative faces that are distinct from the target while identical to the source. To this end, properly disentangling identity and identity-irrelevant information is critical and remains a challenging endeavor. In this work, we propose a novel information disentangling and swapping network, called InfoSwap, to extract the most expressive information for identity representation from a pre-trained face recognition model. The key insight of our method is to formulate the learning of disentangled representations as optimizing an information bottleneck tradeoff, in terms of finding an optimal compression of the pretrained latent features. Moreover, a novel identity contrastive loss is proposed for further disentanglement by requiring a proper distance between the generated identity and the target. While the most prior works have focused on using various loss functions to implicitly guide the learning of representations, we demonstrate that our model can provide explicit supervision for learning disentangled representations, achieving impressive performance in generating more identity-discriminative swapped faces.

关键词： computer vision Face recognition Buildings Detectors Benchmark testing Feature extraction Forgery

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Blocks-World Cameras

Blocks-World Cameras

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

作者： Lee, Jongho Gupta, Mohit Univ Wisconsin Madison Madison WI 53715 USA

ISBN: (纸本)9781665445092

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter.

关键词： Geometry Three-dimensional displays Lighting Cameras Robot sensing systems Sensors pattern recognition

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Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA: A Cause-Effect Look at Language Bias

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

作者： Niu, Yulei Tang, Kaihua Zhang, Hanwang Lu, Zhiwu Hua, Xian-Sheng Wen, Ji-Rong Nanyang Technol Univ Singapore Singapore Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data.

关键词： computer vision Codes Linguistics Robustness Cognition pattern recognition

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Towards Open World Object Detection

Towards Open World Object Detection

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9781665445092

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: 'Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as 'unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.'

关键词： computer vision Protocols Computational modeling Object detection Detectors Benchmark testing pattern recognition

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Distilling vision-Language Pre-Training to Collaborate with Weakly-Supervised Temporal Action Localization

Distilling Vision-Language Pre-Training to Collaborate with ...

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conference on computer vision and pattern recognition (cvpr)

作者： Chen Ju Kunhao Zheng Jinxiang Liu Peisen Zhao Ya Zhang Jianlong Chang Qi Tian Yanfeng Wang CMIC Shanghai Jiao Tong University Huawei Cloud Shanghai AI Laboratory

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between classification and localization, make temporally localized results suffer from the serious incomplete issue. To tackle this issue without additional annotations, this paper considers to distill free action knowledge from vision-Language Pre-training (VLP), as we surprisingly observe that the localization results of vanilla VLP have an over-complete issue, which is just complementary to the CBP results. To fuse such complementarity, we propose a novel distillation-collaboration framework with two branches acting as CBP and VLP respectively. The framework is optimized through a dual-branch alternate training strategy. Specifically, during the B step, we distill the confident background pseudo-labels from the CBP branch; while during the F step, the confident foreground pseudo-labels are distilled from the VLP branch. As a result, the dualbranch complementarity is effectively fused to promote one strong alliance. Extensive experiments and ablation studies on THUMOS14 and ActivityNet1.2 reveal that our method significantly outperforms state-of-the-art methods.

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