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Generative Diffusion Prior for Unified Image Restoration and Enhancement

Generative Diffusion Prior for Unified Image Restoration and...

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

作者： Fei, Ben Lyu, Zhaoyang Pan, Liang Zhang, Junzhe Yang, Weidong Luo, Tianyue Zhang, Bo Dai, Bo Fudan Univ Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Existing image restoration methods mostly leverage the posterior distribution of natural images. However, they often assume known degradation and also require supervised training, which restricts their adaptation to complex real applications. In this work, we propose the Generative Diffusion Prior (GDP) to effectively model the posterior distributions in an unsupervised sampling manner. GDP utilizes a pre-train denoising diffusion generative model (DDPM) for solving linear inverse, non-linear, or blind problems. Specifically, GDP systematically explores a protocol of conditional guidance, which is verified more practical than the commonly used guidance way. Furthermore, GDP is strength at optimizing the parameters of degradation model during the denoising process, achieving blind image restoration. Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions. Experimentally, we demonstrate GDP 's versatility on several image datasets for linear problems, such as super-resolution, deblurring, inpainting, and colorization, as well as non-linear and blind issues, such as low-light enhancement and HDR image recovery. GDP outperforms the current leading unsupervised methods on the diverse benchmarks in reconstruction quality and perceptual quality. Moreover, GDP also generalizes well for natural images or synthesized images with arbitrary sizes from various tasks out of the distribution of the ImageNet training set. The project page is available at https://***/

关键词： Low-level vision

来源：评论

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Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning

Are Binary Annotations Sufficient? Video Moment Retrieval vi...

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

作者： Ji, Wei Liang, Renjie Zheng, Zhedong Zhang, Wenqiao Zhang, Shengyu Li, Juncheng Li, Mengze Chua, Tat-seng Natl Univ Singapore Singapore Singapore Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recent research on video moment retrieval has mostly focused on enhancing the performance of accuracy, efficiency, and robustness, all of which largely rely on the abundance of high-quality annotations. While the precise frame-level annotations are time-consuming and cost-expensive, few attentions have been paid to the labeling process. In this work, we explore a new interactive manner to stimulate the process of human-in-the-loop annotation in video moment retrieval task. The key challenge is to select "ambiguous" frames and videos for binary annotations to facilitate the network training. To be specific, we propose a new hierarchical uncertainty-based modeling that explicitly considers modeling the uncertainty of each frame within the entire video sequence corresponding to the query description, and selecting the frame with the highest uncertainty. Only selected frame will be annotated by the human experts, which can largely reduce the workload. After obtaining a small number of labels provided by the expert, we show that it is sufficient to learn a competitive video moment retrieval model in such a harsh environment. Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection. In general, our active learning strategy for video moment retrieval works not only at the frame level but also at the sequence level. Experiments on two public datasets validate the effectiveness of our proposed method. Our code is released at https://***/renjie-liang/HUAL.

关键词： vision applications and systems

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Exploring Structured Semantic Prior for Multi Label recognition with Incomplete Labels

Exploring Structured Semantic Prior for Multi Label Recognit...

引用

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

作者： Ding, Zixuan Wang, Ao Chen, Hui Zhang, Qiang Liu, Pengzhang Bao, Yongjun Yan, Weipeng Han, Jungong Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China JD Com Beijing Peoples R China Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Sheffield Ctr Machine Intelligence Sheffield S Yorkshire England

ISBN: (纸本)9798350301298

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label correspondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCPNet), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly outperforms existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://***/jameslahm/SCPNet.

关键词： detection recognition: Categorization retrieval

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BAEFormer: Bi-directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

BAEFormer: Bi-directional and Early Interaction Transformers...

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

作者： Pan, Cong He, Yonghao Peng, Junran Zhang, Qian Sui, Wei Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Horizon Robot Beijing Peoples R China Huawei Inc Shenzhen Guangdong Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multiview image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.

关键词： 3D from multi-view and sensors

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ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection

ZBS: Zero-shot Background Subtraction via Instance-level Bac...

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

作者： An, Yongqi Zhao, Xu Yu, Tao Guo, Haiyun Zhao, Chaoyang Tang, Ming Wang, Jinqiao Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories. In this work, we propose an unsupervised BGS algorithm based on zero-shot object detection called Zero-shot Background Subtraction (ZBS). The proposed method fully utilizes the advantages of zero-shot object detection to build the open-vocabulary instance-level background model. Based on it, the foreground can be effectively extracted by comparing the detection results of new frames with the background model. ZBS performs well for sophisticated scenarios, and it has rich and extensible categories. Furthermore, our method can easily generalize to other tasks, such as abandoned object detection in unseen environments. We experimentally show that ZBS surpasses state-of-the-art unsupervised BGS methods by 4.70% F-Measure on the CDnet 2014 dataset. The code is released at https://***/CASIA-IVA-Lab/ZBS.

关键词： and tracking motion Video: Low-level analysis

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Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Learning Semantic-Aware Knowledge Guidance for Low-Light Ima...

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

作者： Wu, Yuhui Pan, Chen Wang, Guoqing Yang, Yang Wei, Jiwei Li, Chongyi Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement

关键词： Low-level vision

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Neuralizer: General Neuroimage Analysis without Re-Training

Neuralizer: General Neuroimage Analysis without Re-Training

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

作者： Czolbe, Steffen Dalca, Adrian V. Univ Copenhagen Copenhagen Denmark MGH Copenhagen Denmark MIT Cambridge MA USA Harvard Med Sch MGH Boston MA USA

ISBN: (纸本)9798350301298

Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. Yet, when presented with a new task or a dataset with different visual characteristics, practitioners most often need to train a new model, or fine-tune an existing one. This is a time-consuming process that poses a substantial barrier for the thousands of neuroscientists and clinical researchers who often lack the resources or machine-learning expertise to train deep learning models. In practice, this leads to a lack of adoption of deep learning, and neuroscience tools being dominated by classical frameworks. We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning. Tasks do not have to be known a priori, and generalization happens in a single forward pass during inference. The model can solve processing tasks across multiple image modalities, acquisition methods, and datasets, and generalize to tasks and modalities it has not been trained on. Our experiments on coronal slices show that when few annotated subjects are available, our multi-task network outperforms task-specific baselines without training on the task.

关键词： cell microscopy Medical and biological vision

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Collaborative Static and Dynamic vision-Language Streams for Spatio-Temporal Video Grounding

Collaborative Static and Dynamic Vision-Language Streams for...

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

作者： Lin, Zihang Tan, Chaolei Hu, Jian-Fang Jin, Zhi Ye, Tiancai Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Peoples R China Tencent Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.

关键词： language reasoning vision

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An In-depth Exploration of Person Re-identification and Gait recognition in Cloth-Changing Conditions

An In-depth Exploration of Person Re-identification and Gait...

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

作者： Li, Weijia Hou, Saihui Zhang, Chunjie Cao, Chunshui Liu, Xu Huang, Yongzhen Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China WATRIX AI Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9798350301298

The target of person re-identification (ReID) and gait recognition is consistent, that is to match the target pedestrian under surveillance cameras. For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions. To tackle this problem, we propose a Cloth-Changing benchmark for Person re-identification and Gait recognition (CCPG). It is a cloth-changing dataset, and there are several highlights in CCPG, (1) it provides 200 identities and over 16K sequences are captured indoors and outdoors, (2) each identity has seven different cloth-changing statuses, which is hardly seen in previous datasets, (3) RGB and silhouettes version data are both available for research purposes. Moreover, aiming to investigate the cloth-changing problem systematically, comprehensive experiments are conducted on video-based ReID and gait recognition methods. The experimental results demonstrate the superiority of ReID and gait recognition separately in different cloth-changing conditions and suggest that gait recognition is a potential solution for addressing the cloth-changing problem. Our dataset will be available at https://***/BNU-IVC/CCPG.

关键词： Datasets and evaluation

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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

GAPartNet: Cross-Category Domain-Generalizable Object Percep...

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

作者： Geng, Haoran Xu, Helin Zhao, Chengyang Xu, Chao Yi, Li Huang, Siyuan Wang, He Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350301298

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world.

关键词： Embodied vision: Active agents simulation

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