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ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

ST3D: Self-training for Unsupervised Domain Adaptation on 3D...

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

作者： Yang, Jihan Shi, Shaoshuai Wang, Zhe Li, Hongsheng Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Xidian Univ Sch CST Xian Shaanxi Peoples R China

ISBN: (纸本)9781665445092

We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds. First, we pre-train the 3D detector on the source domain with our proposed random object scaling strategy for mitigating the negative effects of source domain bias. Then, the detector is iteratively improved on the target domain by alternatively conducting two steps, which are the pseudo label updating with the developed quality-aware triplet memory bank and the model training with curriculum data augmentation. These specific designs for 3D object detection enable the detector to be trained with consistent and high-quality pseudo labels and to avoid overfitting to the large number of easy examples in pseudo labeled data. Our ST3D achieves state-of-the-art performance on all evaluated datasets and even surpasses fully supervised results on KITTI 3D object detection benchmark.

关键词： Training computer vision Three-dimensional displays Codes Pipelines Object detection Detectors

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Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability

Overlooked Factors in Concept-based Explanations: Dataset Ch...

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

作者： Ramaswamy, Vikram V. Kim, Sunnie S. Y. Fong, Ruth Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350301298

Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://***/princetonvisualai/OverlookedFactors

关键词： Explainable computer vision

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JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection

JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, ...

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

作者： Ehsanpour, Mahsa Saleh, Fatemeh Savarese, Silvio Reid, Ian Rezatofighi, Hamid Univ Adelaide Adelaide SA Australia Samsung AI Ctr New York NY USA Stanford Univ Stanford CA 94305 USA Monash Univ Melbourne Vic Australia Australian Natl Univ ANU Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection.

关键词： Visualization Social groups Annotations Pipelines Social robots Robot sensing systems pattern recognition

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Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection

Bayesian Nonparametric Submodular Video Partition for Robust...

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

作者： Sapkota, Hitesh Yu, Qi Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multiple-instance learning (MIL) provides an effective way to tackle the video anomaly detection problem by modeling it as a weakly supervised problem as the labels are usually only available at the video level while missing for frames due to expensive labeling cost. We propose to conduct novel Bayesian non-parametric submodular video partition (BN-SVP) to significantly improve MIL model training that can offer a highly reliable solution for robust anomaly detection in practical settings that include outlier segments or multiple types of abnormal events. BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be naturally interpreted as scenes. Each segment is assumed to be generated through a non-parametric mixture process that allows variations of segments within the same scenes to accommodate the dynamic and noisy nature of many real-world surveillance videos. The scene and mixture component assignment of BN-SVP also induces a pairwise similarity among segments, resulting in non-parametric construction of a submodular set function. Integrating this function with an MIL loss effectively exposes the model to a diverse set of potentially positive instances to improve its training. A greedy algorithm is developed to optimize the submodular function and support efficient model training. Our theoretical analysis ensures a strong performance guarantee of the proposed algorithm. The effectiveness of the proposed approach is demonstrated over multiple real-world anomaly video datasets with robust detection performance.

关键词： Training Upper bound Surveillance Bayes methods pattern recognition Partitioning algorithms Noise measurement

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Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation...

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

作者： Peng, Xiangyu Wang, Kai Zhu, Zheng Wang, Mang You, Yang Natl Univ Singapore Singapore Singapore Tsinghua Univ Beijing Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siamese representation learning, one of the keys is to design good contrastive pairs. Most previous works simply apply random sampling to make different crops of the same image, which overlooks the semantic information that may degrade the quality of views. In this work, we propose ContrastiveCrop, which could effectively generate better crops for Siamese representation learning. Firstly, a semantic-aware object localization strategy is proposed within the training process in a fully unsupervised manner. This guides us to generate contrastive views which could avoid most false positives (i.e., object vs. background). Moreover, we empirically find that views with similar appearances are trivial for the Siamese model training. Thus, a center-suppressed sampling is further designed to enlarge the variance of crops. Remarkably, our method takes a careful consideration of positive pairs for contrastive learning with negligible extra training overhead. As a plug-and-play and framework-agnostic module, ContrastiveCrop consistently improves SimCLR, MoCo, BYOL, SimSiam by 0.4% similar to 2.0% classification accuracy on CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also achieved on downstream detection and segmentation tasks when pre-trained on ImageNet-1K.

关键词： Representation learning Training Location awareness Image segmentation computer vision Semantics Crops

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Deep Optimized Priors for 3D Shape Modeling and Reconstruction

Deep Optimized Priors for 3D Shape Modeling and Reconstructi...

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

作者： Yang, Mingyue Wen, Yuxin Chen, Weikai Chen, Yongwei Jia, Kui South China Univ Technol Guangzhou Peoples R China Tencent Game AI Res Ctr Shenzhen Peoples R China Pazhou Lab Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D modeling and reconstruction that greatly improves the generalization ability of a deep generator. Our approach strives to connect the good ends of both learning-based and optimization-based methods. In particular, unlike the common practice that fixes the pre-trained priors at test time, we propose to further optimize the learned prior and latent code according to the input physical measurements after the training. We show that the proposed strategy effectively breaks the barriers constrained by the pre-trained priors and could lead to high-quality adaptation to unseen data. We realize our framework using the implicit surface representation and validate the efficacy of our approach in a variety of challenging tasks that take highly sparse or collapsed observations as input. Experimental results show that our approach compares favorably with the state-of-the-art methods in terms of both generality and accuracy.

关键词： Training Solid modeling computer vision Three-dimensional displays Shape Particle measurements Time measurement

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TikTok for good: Creating a diverse emotion expression database

TikTok for good: Creating a diverse emotion expression datab...

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

作者： Surabhi, Saimourya Shah, Bhavik Washington, Peter Mutlu, Onur Cezmi Leblanc, Emilie Mohite, Prathamesh Husic, Arman Kline, Aaron Dunlap, Kaitlyn McNealis, Maya Liu, Bennett Deveaux, Nick Sleiman, Essam Wall, Dennis P. Stanford Univ Dept Biomed Data Sci Dept Pediat Syst Med Stanford CA 94305 USA Stanford Univ Dept Psychiat & Behav Sci Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition (FER) is a critical computer vision task for a variety of applications. Despite the widespread use of FER, there is a dearth of racially diverse facial emotion datasets which are enriched for children, teens, and adults. To bridge this gap, we have built a diverse expression recognition database using publicly available videos from TikTok, a video-focused social networking service. We describe the construction of the TikTok Facial expression recognition (FER) database. The dataset is extracted from 6428 videos scraped from TikTok. The videos consist of 9392 distinct individuals and labels for 15 emotion-related prompts. We were able to achieve a F1 score 0.78 for Ekman emotions on expression classification using transfer learning. We hope that the scale and diversity of the TikTokFER dataset will be of use to affective computing practitioners.

关键词： Bridges computer vision Emotion recognition Databases Social networking (online) Face recognition conferences

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

作者： Ju, Chen Zheng, Kunhao Liu, Jinxiang Zhao, Peisen Zhang, Ya Chang, Jianlong Tian, Qi Wang, Yanfeng Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Huawei Cloud Shenzhen Peoples R China

ISBN: (纸本)9798350301298

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 dual-branch 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.

关键词： Video: Action and event understanding

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MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

MobileDets: Searching for Object Detection Architectures for...

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

作者： Xiong, Yunyang Liu, Hanxiao Gupta, Suyog Akin, Berkin Bender, Gabriel Wang, Yongzhe Kindermans, Pieter-Jan Tan, Mingxing Singh, Vikas Chen, Bo Univ Wisconsin Madison WI 53705 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions. We discover that regular convolutions are a potent component to boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators. On the COCO object detection task, MobileDets outperform MobileNetV3+SSDLite by 1.7 mAP at comparable mobile CPU inference latencies. MobileDets also outperform MobileNetV2+SSDLite by 1.9 mAP on mobile CPUs, 3.7 mAP on Google EdgeTPU, 3.4 mAP on Qual-comm Hexagon DSP and 2.7 mAP on Nvidia Jetson GPU without increasing latency. Moreover MobileDets are comparable with the state-of-the-art MnasFPN on mobile CPUs even without using the feature pyramid, and achieve better mAP scores on both EdgeTPUs and DSPs with up to 2x speedup. Code and models are available in the TensorFlow Object Detection API [16]: https://***/tensor flow/models/tree/master/research/object_detection.

关键词： Convolutional codes Image edge detection Neural networks Object detection computer architecture Network architecture Search problems

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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversari...

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

作者： Wang, Sibo Zhang, Jie Yuan, Zheng Shan, Shiguang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However, direct application to the CLIP model may result in overfitting, compromising the model's capacity for generalization. In this paper, we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method, which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch, to enhance the model's zero-shot adversarial robustness. Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outper-forms the state-of-the-art method, improving the top-1 robust accuracy by an average of 4.99%. Furthermore, our approach consistently improves clean accuracy by an aver-age of 8.72%. Our code is available at here.(1)

关键词： Adversarial Robustness Large-scale vision-language models Zero-Shot

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