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Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Informative and Consistent Correspondence Mining for Cross-D...

引用

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

作者： Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.

关键词： computer vision Annotations Collaboration Object detection Detectors Generators pattern recognition

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Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration 32

Dual Residual Networks Leveraging the Potential of Paired Op...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Xing Suganuma, Masanori Sun, Zhun Okatani, Takayuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan RIKEN Ctr AIP Tokyo Japan

ISBN: (纸本)9781728132938

In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of pairedoperations, e.g., upand down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style;it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by Veit et al., we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.

关键词： Computational Photography Deep Learning Image and Video Synthesis Low-level vision vision Applications and Syst

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DVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses

DVMNet: Computing Relative Pose for Unseen Objects Beyond Hy...

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

作者： Zhao, Chen Zhang, Tong Dang, Zheng Salzmann, Mathieu Ecole Polytech Fed Lausanne Lausanne Switzerland ClearSpace SA Renens Switzerland

ISBN: (纸本)9798350353006

Determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically approximate the continuous pose representation with a large number of discrete pose hypotheses, which incurs a computationally expensive process of scoring each hypothesis at test time. By contrast, we present a Deep Voxel Matching Network (DVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. To this end, we map the two input RGB images, reference and query, to their respective voxelized 3D representations. We then pass the resulting voxels through a pose estimation module, where the voxels are aligned and the pose is computed in an end-to-end fashion by solving a least-squares problem. To enhance robustness, we introduce a weighted closest voxel algorithm capable of mitigating the impact of noisy voxels. We conduct extensive experiments on the CO3D, LINEMOD, and Objaverse datasets, demonstrating that our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods. Our code is released at: https://***/sailor-z/DVMNet/.

关键词： 3D computer vision Multi-View Geometry Object Pose Estimation

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Driver state monitor from DELPHI

Driver state monitor from DELPHI

引用

conference on computer vision and pattern recognition

作者： Edenborough, N Hammoud, R Harbach, A Ingold, A Kisacanin, B Malawey, P Newman, T Scharenbroch, G Skiver, S Smith, M Wilhelm, A Witt, G Yoder, E Zhang, H Delphi Electronics and Safety United States

ISBN: (纸本)0769523722

We present an automotive-grade, real-time, vision-based Driver State Monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.

关键词： computer vision

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3DAC: Learning Attribute Compression for Point Clouds

3DAC: Learning Attribute Compression for Point Clouds

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

作者： Fang, Guangchi Hu, Qingyong Wang, Hanyun Xu, Yiling Guo, Yulan Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Univ Oxford Oxford England Informat Engn Univ Zhengzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed method.

关键词： Point cloud compression Reflectivity computer vision Three-dimensional displays Correlation Transforms Entropy

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Tracking multiple mouse contours (without too many samples)

Tracking multiple mouse contours (without too many samples)

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conference on computer vision and pattern recognition

作者： Branson, K Belongie, S Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA

ISBN: (纸本)0769523722

We present a particle filtering algorithm for robustly tracking the contours of multiple deformable objects through severe occlusions. Our algorithm combines a multiple blob tracker with a contour tracker in a manner that keeps the required number of samples small. This is a natural combination because both algorithms have complementary strengths. The multiple blob tracker uses a natural multitarget model and searches a smaller and simpler space. On the other hand, contour tracking gives more fine-tuned results and relies on cues that are available during severe occlusions. Our choice of combination of these two algorithms accentuates the advantages of each. We demonstrate good performance on challenging video of three identical mice that contains multiple instances of severe occlusion.

关键词： Mice (computer peripherals)

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KNN Local Attention for Image Restoration

KNN Local Attention for Image Restoration

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

作者： Lee, Hunsang Choi, Hyesong Sohn, Kwanghoon Min, Dongbo Yonsei Univ Seoul South Korea Ewha W Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent works attempt to integrate the non-local operation with CNNs or Transformer, achieving remarkable performance in image restoration tasks. The global similarity, however, has the problems of the lack of locality and the high computational complexity that is quadratic to an input resolution. The local attention mechanism alleviates these issues by introducing the inductive bias of the locality with convolution-like operators. However, by focusing only on adjacent positions, the local attention suffers from an insufficient receptive field for image restoration. In this paper, we propose a new attention mechanism for image restoration, called k-NN Image Transformer (KiT), that rectifies the above mentioned limitations. Specifically, the KiT groups k-nearest neighbor patches with locality sensitive hashing (LSH), and the grouped patches are aggregated into each query patch by performing a pair-wise local attention. In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i.e., inductive bias of locality and linear complexity to input resolution. The proposed method outperforms state-of-the-art restoration approaches on image denoising, deblurring and deraining benchmarks. The code will be available soon.

关键词： Image color analysis Focusing Machine learning Transformers Image restoration pattern recognition Task analysis

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Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

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

作者： Wang, Lianyu Wang, Meng Zhang, Daoqiang Fu, Huazhu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Agcy Sci Technol & Res STAR IHPC Singapore 138632 Singapore

ISBN: (纸本)9798350301298

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks cross-domain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

关键词： accountability ethics in vision fairness privacy Transparency

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DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal 30

DeshadowNet: A Multi-context Embedding Deep Network for Shad...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qu, Liangqiong Tian, Jiandong He, Shengfeng Tang, Yandong Lau, Rynson W. H. Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.

关键词： Semantics Feature extraction Lighting Image color analysis Benchmark testing computer vision Benchmark testing image colour analysis shadows Semantics images computer vision Lighting Feature extraction Online resources parallel network semiotics BENCHMARKS embedding

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Towards Unsupervised Domain Generalization

Towards Unsupervised Domain Generalization

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

作者： Zhang, Xingxuan Zhou, Linjun Xu, Renzhe Cui, Peng Shen, Zheyan Liu, Haoxin Tsinghua Univ Dept Comp Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains. The performances of current DG methods largely rely on sufficient labeled data, which are usually costly or unavailable, however. Since unlabeled data are far more accessible, we seek to explore how unsupervised learning can help deep models generalize across domains. Specifically, we study a novel generalization problem called unsupervised domain generalization (UDG), which aims to learn generalizable models with unlabeled data and analyze the effects of pre-training on DG. In UDG, models are pre-trained with unlabeled data from various source domains before being trained on labeled source data and eventually tested on unseen target domains. Then we propose a method named Domain-Aware Representation LearnING (DARLING) to cope with the significant and misleading heterogeneity within unlabeled pretraining data and severe distribution shifts between source and target data. Surprisingly we observe that DARLING can not only counterbalance the scarcity of labeled data but also further strengthen the generalization ability of models when the labeled data are insufficient. As a pretraining approach, DARLING shows superior or comparable performance compared with ImageNet pretraining protocol even when the available data are unlabeled and of a vastly smaller amount compared to ImageNet, which may shed light on improving generalization with large-scale unlabeled data.

关键词： Representation learning Analytical models computer vision Protocols Computational modeling Data models pattern recognition

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