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Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection

Density-Insensitive Unsupervised Domain Adaption on 3D Objec...

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conference on computer vision and pattern recognition (CVPR)

作者： Qianjiang Hu Daizong Liu Wei Hu Wangxuan Institute of Computer Technology Peking University Beijing China

3D object detection from point clouds is crucial in safety-critical autonomous driving. Although many works have made great efforts and achieved significant progress on this task, most of them suffer from expensive annotation cost and poor transferability to unknown data due to the domain gap. Recently, few works attempt to tackle the domain gap in objects, but still fail to adapt to the gap of varying beam-densities between two domains, which is critical to mitigate the characteristic differences of the LiDAR collectors. To this end, we make the attempt to propose a density-insensitive domain adaption framework to address the density-induced domain gap. In particular, we first introduce Random Beam Re-Sampling (RBRS) to enhance the robustness of 3D detectors trained on the source domain to the varying beam-density. Then, we take this pre-trained detector as the backbone model, and feed the unlabeled target domain data into our newly designed task-specific teacher-student framework for predicting its high-quality pseudo labels. To further adapt the property of density-insensitivity into the target domain, we feed the teacher and student branches with the same sample of different densities, and propose an Object Graph Alignment (OGA) module to construct two object-graphs between the two branches for enforcing the consistency in both the attribute and relation of cross-density objects. Experimental results on three widely adopted 3D object detection datasets demonstrate that our proposed domain adaption method outperforms the state-of-the-art methods, especially over varying-density data. Code is available at https://***/WoodwindHu/DTS.

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Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos

Efficient Semantic Segmentation by Altering Resolutions for ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yubin Hu Yuze He Yanghao Li Jisheng Li Yuxing Han Jiangtao Wen Yong-Jin Liu Department of Computer Science and Technology BNRist Tsinghua University Shenzhen International Graduate School Tsinghua University Eastern Institute for Advanced Study

Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost from the input side: the input resolution. In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS. AR-Seg aims to reduce the computational cost by using low resolution for non-keyframes. To prevent the performance degradation caused by downsampling, we design a Cross Resolution Feature Fusion (CR-eFF) module, and supervise it with a novel Feature Similarity Training (FST) strategy. Specifically, CReFF first makes use of motion vectors stored in a compressed video to warp features from high-resolution keyframes to low-resolution non-keyframes for better spatial alignment, and then selectively aggregates the warped features with local attention mechanism. Furthermore, the proposed FST supervises the aggregated features with high-resolution features through an explicit similarity loss and an implicit constraint from the shared decoding layer. Extensive experiments on CamVid and Cityscapes show that AR-Seg achieves state-of-the-art performance and is compatible with different segmentation backbones. On CamVid, AR-Seg saves 67% computational cost (measured in GFLOPs) with the PSPNet18 back-bone while maintaining high segmentation accuracy. Code: https://***/THU-LYJ-Lab/AR-Seg.

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Implicit Identity Driven Deepfake Face Swapping Detection

Implicit Identity Driven Deepfake Face Swapping Detection

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conference on computer vision and pattern recognition (CVPR)

作者： Baojin Huang Zhongyuan Wang Jifan Yang Jiaxin Ai Qin Zou Qian Wang Dengpan Ye NERCMS School of Computer Science Wuhan University School of Cyber Science and Engineering Wuhan University

In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit identity and implicit identity, which respectively corresponds to the identity of the source face and target face during face swapping. Note that the explicit identities of faces can be extracted by regular face recognizers. Particularly, the implicit identity of real face is consistent with the its explicit identity. Thus the difference between explicit and implicit identity of face facilitates face swapping detection. Following this idea, we propose a novel implicit identity driven framework for face swapping detection. Specifically, we design an explicit identity contrast (EIC) loss and an implicit identity exploration (IIE) loss, which supervises a CNN backbone to embed face images into the implicit identity space. Under the guidance of EIC, real samples are pulled closer to their explicit identities, while fake samples are pushed away from their explicit identities. More-over, IIE is derived from the margin-based classification loss function, which encourages the fake faces with known target identities to enjoy intra-class compactness and inter-class diversity. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art counterparts.

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Task-Specific Fine-Tuning via Variational Information Bottleneck for Weakly-Supervised Pathology Whole Slide Image Classification

Task-Specific Fine-Tuning via Variational Information Bottle...

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conference on computer vision and pattern recognition (CVPR)

作者： Honglin Li Chenglu Zhu Yunlong Zhang Yuxuan Sun Zhongyi Shui Wenwei Kuang Sunyi Zheng Lin Yang College of Computer Science and Technology Zhejiang University School of Engineering Westlake University Research Center for Industries of the Future The University of Hong Kong

While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and generalization problems due to high computational costs and limited supervision of Gigapixel WSIs. To deal with the computation problem, previous methods utilize a frozen model pretrained from ImageNet to obtain representations, however, it may lose key information owing to the large domain gap and hinder the generalization ability without image-level training-time augmentation. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the downstream task-specific features via partial label tuning are not explored. To alleviate this problem, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. We evaluate the method on five pathological WSI datasets on various WSI heads. The experimental results show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://***/invoker-LL/WSI-finetuning.

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CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

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

作者： Wang, Zhengwei She, Qi Chalasani, Tejo Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781728193601

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： computer vision

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The 1st Agriculture-vision Challenge: Methods and Results

The 1st Agriculture-Vision Challenge: Methods and Results

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

作者： Chiu, Mang Tik Xu, Xingqian Wang, Kai Hobbs, Jennifer Hovakimyan, Naira Huang, Thomas S. Shi, Honghui Wei, Yunchao Huang, Zilong Schwing, Alexander Brunner, Robert Dozier, Ivan Dozier, Wyatt Ghandilyan, Karen Wilson, David Park, Hyunseong Kim, Junhee Kim, Sungho Liu, Qinghui Kampffmeyer, Michael C. Jenssen, Robert Salberg, Arnt B. Barbosa, Alexandre Trevisan, Rodrigo Zhao, Bingchen Yu, Shaozuo Yang, Siwei Wang, Yin Sheng, Hao Chen, Xiao Su, Jingyi Rajagopal, Ram Ng, Andrew Van Thong Huynh Kim, Soo-Hyung Na, In-Seop Baid, Ujjwal Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Tang, Jianyu UIUC Urbana IL 61801 USA Intelinair Indianapolis IN USA Univ Oregon Eugene OR 97403 USA Agcy Def Dev Seoul South Korea DGIST Daegu South Korea Norwegian Comp Ctr Oslo Norway UiT Arctic Univ Norway Oslo Norway Tongji Univ Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chegg Inc Santa Clara CA USA Chonnam Natl Univ Gwangju South Korea Chosun Univ Gwangju South Korea SGGS Inst Engn & Technol Nanded India Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

The first Agriculture-vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task;the link can be found here.

关键词： Semantics Image segmentation pattern recognition conferences Agriculture Computational modeling computer vision

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Long-Tailed Visual recognition via Self-Heterogeneous Integration with Knowledge Excavation

Long-Tailed Visual Recognition via Self-Heterogeneous Integr...

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conference on computer vision and pattern recognition (CVPR)

作者： Yan Jin Mengke Li Yang Lu Yiu-ming Cheung Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China Department of Computer Science Hong Kong Baptist University Hong Kong SAR China

Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution. Experts in these methods are with the same model depth, which neglects the fact that different classes may have different preferences to be fit by models with different depths. To this end, we propose a novel MoE-based method called Self-Heterogeneous Integration with Knowledge Excavation (SHIKE). We first propose Depth-wise Knowledge Fusion (DKF) to fuse features between different shallow parts and the deep part in one network for each expert, which makes experts more diverse in terms of representation. Based on DKF, we further propose Dynamic Knowledge Transfer (DKT) to reduce the influence of the hardest negative class that has a non-negligible impact on the tail classes in our MoEframework. As a result, the classification accuracy of long-tailed data can be significantly improved, especially for the tail classes. SHIKE achieves the state-of-the-art performance of 56.3%, 60.3%, 75.4% and 41.9% on CIFAR100-LT (IF100), ImageNet-LT, iNaturalist 2018, and Places-LT, respectively. The source code is available at https://***/jinyan-06/SHIKE.

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Adaptive Zone-aware Hierarchical Planner for vision-Language Navigation

Adaptive Zone-aware Hierarchical Planner for Vision-Language...

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conference on computer vision and pattern recognition (CVPR)

作者： Chen Gao Xingyu Peng Mi Yan He Wang Lirong Yang Haibing Ren Hongsheng Li Si Liu Institute of Artificial Intelligence Beihang University CFCS and School of EECS Peking University Meituan The Chinese University of Hong Kong Hangzhou Innovation Institute Beihang University

The task of vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of sub-goals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and sub-goal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R).

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PHA: Patch-Wise High-Frequency Augmentation for Transformer-Based Person Re-Identification

PHA: Patch-Wise High-Frequency Augmentation for Transformer-...

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conference on computer vision and pattern recognition (CVPR)

作者： Guiwei Zhang Yongfei Zhang Tianyu Zhang Bo Li Shiliang Pu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University Pengcheng Laboratory Hikvision Research Institute

Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into vision Transformers (ViTs) can improve the performance of person reidentification, the rationale behind it remains elusive. From a frequency perspective, we reveal that ViTs perform worse than CNNs in preserving key high-frequency components (e.g, clothes texture details) since high-frequency components are inevitably diluted by low-frequency ones due to the intrinsic Self-Attention within ViTs. To remedy such inadequacy of the ViT, we propose a Patch-wise High-frequency Augmentation (PHA) method with two core designs. First, to enhance the feature representation ability of high-frequency components, we split patches with high-frequency components by the Discrete Haar Wavelet Transform, then empower the ViT to take the split patches as auxiliary input. Second, to prevent high-frequency components from being diluted by low-frequency ones when taking the entire sequence as input during network optimization, we propose a novel patch-wise contrastive loss. From the view of gradient optimization, it acts as an implicit augmentation to improve the representation ability of key high-frequency components. This benefits the ViT to capture key high-frequency components to extract discriminative person representations. PHA is necessary during training and can be removed during inference, without bringing extra complexity. Extensive experiments on widely-used ReID datasets validate the effectiveness of our method.

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Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models

Augmentation of Atmospheric Turbulence Effects on Thermal Ad...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Engin Uzun Ahmet Anı l Dursun Erdem Akagü ndü z Dept. of Image Proc. &amp Computer Vis. Technologies Aselsan Inc. Turkey Graduate School of Informatics Middle East Technical University Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.

关键词： Adaptation models Atmospheric modeling Wind speed Training data Refractive index Imaging Object detection

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