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HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging

HerosNet: Hyperspectral Explicable Reconstruction and Optima...

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

作者： Zhang, Xuanyu Zhang, Yongbing Xiong, Ruiqin Sun, Qilin Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Harbin Inst Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral cameras that are either slow, expensive, or bulky, reconstructing hyperspectral images (HSIs) from a low-budget snapshot measurement has drawn wide attention. By mapping a truncated numerical optimization algorithm into a network with a fixed number of phases, recent deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI) have achieved remarkable success. However, DUNs are far from reaching the scope of industrial applications limited by the lack of cross-phase feature interaction and adaptive parameter adjustment. In this paper, we propose a novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network for SCI, dubbed HerosNet, which includes several phases under the ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix and contextually adjust the step size in the gradient descent step, and hierarchically fuse and interact the hidden states of previous phases to effectively recover current HSI frames in the proximal mapping step. Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end to further improve the reconstruction performance. Finally, our HerosNet is validated to outperform the state-of-the-art methods on both simulation and real datasets by large margins. The source code is available at https://***/jianzhangcs/HerosNet.

关键词： Photography Deep learning Correlation Fuses Sensors pattern recognition Iterative methods

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Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

Non-Salient Region Object Mining for Weakly Supervised Seman...

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

作者： Yao, Yazhou Chen, Tao Xie, Guo-Sen Zhang, Chuanyi Shen, Fumin Wu, Qi Tang, Zhenmin Zhang, Jian Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Univ Elect Sci & Technol China Chengdu Peoples R China Univ Adelaide Adelaide SA Australia Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

关键词： Image segmentation computer vision Codes Annotations Image edge detection Semantics Cognition

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Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning

Removing the Background by Adding the Background: Towards Ba...

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

作者： Wang, Jinpeng Gao, Yuting Li, Ke Lin, Yiqi Ma, Andy J. Cheng, Hao Peng, Pai Huang, Feiyue Ji, Rongrong Sun, Xing Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Tencent Youtu Lab Shenzhen Guangdong Peoples R China Xiamen Univ Xiamen Fujian Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To mitigate the model reliance towards the background, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. We term our method as Background Erasing (BE). It is worth noting that the implementation of our method is so simple and neat and can be added to most of the SOTA methods without much efforts. Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased dataset Diving48.

关键词： Deep learning computer vision Force Focusing Resists Predictive models Benchmark testing

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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Hierarchical Pyramid Diverse Attention Networks for Face recognition

Hierarchical Pyramid Diverse Attention Networks for Face Rec...

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

作者： Wang, Qiangchang Wu, Tianyi Zheng, He Guo, Guodong West Virginia Univ Morgantown WV 26506 USA Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China Baidu Beijing Peoples R China

ISBN: (纸本)9781728171685

Deep learning has achieved a great success in face recognition (FR), however, few existing models take hierarchical multi-scale local features into consideration. In this work, we propose a hierarchical pyramid diverse attention (HPDA) network. First, it is observed that local patches would play important roles in FR when the global face appearance changes dramatically. Some recent works apply attention modules to locate local patches automatically without relying on face landmarks. Unfortunately, without considering diversity, some learned attentions tend to have redundant responses around some similar local patches, while neglecting other potential discriminative facial parts. Meanwhile, local patches may appear at different scales due to pose variations or large expression changes. To alleviate these challenges, we propose a pyramid diverse attention (PDA) to learn multi-scale diverse local representations automatically and adaptively. More specifically, a pyramid attention is developed to capture multi-scale features. Meanwhile, a diverse learning is developed to encourage models to focus on different local patches and generate diverse local features. Second, almost all existing models focus on extracting features from the last convolutional layer, lacking of local details or small-scale face parts in lower layers. Instead of simple concatenation or addition, we propose to use a hierarchical bilinear pooling (HBP) to fuse information from multiple layers effectively. Thus, the HPDA is developed by integrating the PDA into the HBP. Experimental results on several datasets show the effectiveness of the HPDA, compared to the state-of-the-art methods.

关键词： Face recognition

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Align and Prompt: Video-and-Language Pre-training with Entity Prompts

Align and Prompt: Video-and-Language Pre-training with Entit...

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

作者： Li, Dongxu Li, Junnan Li, Hongdong Niebles, Juan Carlos Hoi, Steven C. H. Salesforce Res San Francisco CA 94105 USA Australian Natl Univ Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a standard transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning fine-grained visual-language alignment usually requires off-the-shelf object detectors to provide object information, which is bottlenecked by the detector's limited vocabulary and expensive computation cost. In this paper, we propose Align and Prompt: a new video-and-language pre-training framework ( ALPRO), which operates on sparsely-sampled video frames and achieves more effective cross-modal alignment without explicit object detectors. First, we introduce a video-text contrastive (VTC) loss to align unimodal video-text features at the instance level, which eases the modeling of cross-modal interactions. Then, we propose a novel visually-grounded pre-training task, prompting entity modeling (PEM), which learns fine-grained alignment between visual region and text entity via an entity prompter module in a self-supervised way. Finally, we pretrain the video-and-language transformer models on large webly-source video-text pairs using the proposed VTC and PEM losses as well as two standard losses of masked language modeling (MLM) and video-text matching (VTM). The resulting pre-trained model achieves state-of-the-art performance on both text-video retrieval and videoQA, outperforming prior work by a substantial margin. Implementation and pre-trained models are available at https://***/salesforce/ALPRO.

关键词： Representation learning Vocabulary Visualization Semantics Detectors Transformers pattern recognition

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D2-Net: A Trainable CNN for Joint Description and Detection of Local Features 32

D2-Net: A Trainable CNN for Joint Description and Detection ...

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

作者： Dusmanu, Mihai Rocco, Ignacio Pajdla, Tomas Pollefeys, Marc Sivic, Josef Torii, Akihiko Sattler, Torsten PSL Res Univ CNRS ENS DI F-75005 Paris France Inria Rocquencourt France Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland CTU CIIRC Prague Czech Republic Microsoft Redmond WA USA Tokyo Inst Technol Tokyo Japan Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781728132938

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector By postponing the detection to a later stage, the obtained keypoints are more stable than their traditionalcounterparts based on early detection of low-level structures. We show that this model can be trainedusing pixel correspondences extractedfrom readilyavailable large-scaleSfM reconstructions, without any further annotations. The proposed method obtains state-of-the-artperformance on both the difficult Aachen Day-Night localizationdataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarksfor image matching and 3D reconstruction.

关键词： 3D from Multiview and Sensors Categorization Deep Learning Low-level vision recognition: Detection Retrieval

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Discovering Objects that Can Move

Discovering Objects that Can Move

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

作者： Bao, Zhipeng Tokmakov, Pavel Jabri, Allan Wang, Yu-Xiong Gaidon, Adrien Hebert, Martial CMU Pittsburgh PA 15213 USA Toyota Res Inst Tokyo Japan Univ Calif Berkeley Berkeley CA USA UIUC Champaign IL USA TRI Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper studies the problem of object discovery - separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects from the background in cluttered scenes. This is a fundamental limitation since the definition of an object is inherently ambiguous and context-dependent. To resolve this ambiguity, we choose to focus on dynamic objects - entities that can move independently in the world We then scale the recent auto-encoder based frameworks for unsupervised object discovery from toy synthetic images to complex real-world scenes. 'lb this end, we simplify their architecture, and augment the resulting model with a weak learning signal from general motion segmentation algorithms. Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects. We show that our model scales to a newly collected, photo-realistic synthetic dataset with street driving scenarios. Additionally, we leverage ground truth segmentation and flow annotations in this dataset for thorough ablation and evaluation. Finally, our experiments on the real-world KITTI benchmark demonstrate that the proposed approach outperforms both heuristic- and learning-based methods by capitalizing on motion cues.

关键词： Representation learning Learning systems computer vision Tracking Shape Motion segmentation Dynamics

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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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Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

Cross-Domain Adaptive Clustering for Semi-Supervised Domain ...

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

作者： Li, Jichang Li, Guanbin Shi, Yemin Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by labeled samples from the source domain. This could lead to disconnection between the labeled and unlabeled target samples as well as misalignment between unlabeled target samples and the source domain. In this paper, we propose a novel approach called Cross-domain Adaptive Clustering to address this problem. To achieve both inter-domain and intra-domain adaptation, we first introduce an adversarial adaptive clustering loss to group features of unlabeled target data into clusters and perform cluster-wise feature alignment across the source and target domains. We further apply pseudo labeling to unlabeled samples in the target domain and retain pseudo-labels with high confidence. Pseudo labeling expands the number of "labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning. Extensive experiments on benchmark datasets, including DomainNet, Office-Home and Office, demonstrate that our proposed approach achieves the state-of-the-art performance in semi-supervised domain adaptation.

关键词： Training Adaptation models computer vision Training data Benchmark testing Data models Adversarial machine learning

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