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GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency

GradICON: Approximate Diffeomorphisms via Gradient Inverse C...

引用

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

作者： Tian, Lin Greer, Hastings Vialard, Francois-Xavier Kwitt, Roland Estepar, Raul San Jose Rushmore, Richard Jarrett Makris, Nikolaos Bouix, Sylvain Niethammer, Marc Univ N Carolina Marlborough MA 01752 USA Univ Gustave Eiffel LIGM Marne La Vallee France INRIA Paris MOKAPLAN Paris France Univ Salzburg Salzburg Austria Harvard Med Sch Boston MA USA Boston Univ Boston MA USA ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350301298

We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer - GradICON results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. Code is available at https://***/uncbiag/ICON.

关键词： cell microscopy Medical and biological vision

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Burstormer: Burst Image Restoration and Enhancement Transformer

Burstormer: Burst Image Restoration and Enhancement Transfor...

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

作者： Dudhane, Akshay Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Yang, Ming-Hsuan Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Linkoping Univ Linkoping Sweden Univ Calif Merced Merced CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain multiple degradations. The challenge is to properly align the successive image shots and merge their complimentary information to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication in the burst neighborhoodsf or information aggregation and progressive fusion while modeling the burst-wide context. However, the input burst frames need to be properly aligned before fusing their information. Therefore, we propose an enhanced deformable alignment module for aligning burst features with regards to the reference frame. Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges feature information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions. Aft er multi-level alignment and enrichment, we re-emphasize on inter-frame communication within burst using a cyclic burst sampling module. Finally, the inter-frame information is aggregated using the proposed burst feature fusion module followed by progressive upsampling. Our Burstormer outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhancement. Our codes and pre-trained models are available at https://***/akshaydudhane16/Burstormer.

关键词： Low-level vision

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Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization

Learning to Exploit the Sequence-Specific Prior Knowledge fo...

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

作者： Qin, Haina Han, Longfei Xiong, Weihua Wang, Juan Ma, Wentao Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China Zeku Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks.

关键词： Low-level vision

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RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

RIDCP: Revitalizing Real Image Dehazing via High-Quality Cod...

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

作者： Wu, Rui-Qi Duan, Zheng-Peng Guo, Chun-Le Chai, Zhi Li, Chongyi Nankai Univ VCIP CS Tianjin Peoples R China Hisilicon Technol Co Ltd Shenzhen Guangdong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing. Code and data are released at https://***/projects/RIDCP.

关键词： Low-level vision

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Learning A Sparse Transformer Network for Effective Image Deraining

Learning A Sparse Transformer Network for Effective Image De...

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

作者： Chen, Xiang Li, Hao Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://***/cschenxiang/DRSformer.

关键词： Low-level vision

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Semi-DETR: Semi-Supervised Object Detection with Detection Transformers

Semi-DETR: Semi-Supervised Object Detection with Detection T...

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

作者： Zhang, Jiacheng Lin, Xiangru Zhang, Wei Wang, Kuo Tan, Xiao Han, Junyu Ding, Errui Wang, Jingdong Li, Guanbin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Sun Yat Sen Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate, leading to training inefficiency;(2) DETR-based detectors lack deterministic correspondence between the input query and its prediction output, which hinders the applicability of the consistency-based regularization widely used in current SSOD methods. We present Semi-DETR, the first transformer-based end-to-end semi-supervised object detector, to tackle these problems. Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage. Besides, we introduce a Cross-view Query Consistency method to learn the semantic feature invariance of object queries from different views while avoiding the need to find deterministic query correspondence. Furthermore, we propose a Cost-based Pseudo Label Mining module to dynamically mine more pseudo boxes based on the matching cost of pseudo ground truth bounding boxes for consistency training. Extensive experiments on all SSOD settings of both COCO and Pascal VOC benchmark datasets show that our Semi-DETR method outperforms all state-of-the-art methods by clear margins.

关键词： detection recognition: Categorization retrieval

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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

作者： Sun, Kexin Chen, Zhineng Wang, Gongwei Iu, Jun Ye, Xiongjun Gang, Yu -Gang Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Peking Univ Peoples Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Dept Urol Natl Canc Ctr Canc Hosp Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Canc Hosp Natl Clin Res Ctr Canc Beijing Peoples R China

ISBN: (纸本)9798350301298

The cost of pathological examination makes virtual re-staining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual re-staining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple post-processing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bi-directional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the interpatch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

关键词： cell microscopy Medical and biological vision

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CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

CAT: LoCalization and IdentificAtion Cascade Detection Trans...

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

作者： Ma, Shuailei Wang, Yuefeng Wei, Ying Fan, Jiaqi Li, Thomas H. Liu, Hongli Lv, Fanbing Northeast Univ Shenyang Peoples R China Peking Univ Informat Technol R&D Innovat Ctr Shenzhen Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Changsha Hisense Intelligent Syst Res Inst Co Ltd Changsha Peoples R China

ISBN: (纸本)9798350301298

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Experiments on two benchmarks, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art methods. The code is publicly available at https://***/xiaomabufei/CAT.

关键词： detection recognition: Categorization retrieval

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Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization

Devil is in the Queries: Advancing Mask Transformers for Rea...

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

作者： Yuan, Mingze Xia, Yingda Dong, Hexin Chen, Zifan Yao, Jiawen Qiu, Mingyan Yan, Ke Yin, Xiaoli Shi, Yu Chen, Xin Liu, Zaiyi Dong, Bin Zhou, Jingren Lu, Le Zhang, Ling Zhang, Li Alibaba Grp Hangzhou Peoples R China Peking Univ Beijing Peoples R China Guangdong Prov Peoples Hosp Guangzhou Peoples R China Shengjing Hosp Shenyang Peoples R China Peking Univ Changsha Inst Comp & Digital Econ Beijing Peoples R China

ISBN: (纸本)9798350301298

Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant. A trustworthy medical AI algorithm should demonstrate its effectiveness on tail conditions to avoid clinically dangerous damage in these out-of-distribution (OOD) cases. In this paper, we adopt the concept of object queries in Mask Transformers to formulate semantic segmentation as a soft cluster assignment. The queries fit the feature-level cluster centers of inliers during training. Therefore, when performing inference on a medical image in real-world scenarios, the similarity between pixels and the queries detects and localizes OOD regions. We term this OOD localization as MaxQuery. Furthermore, the foregrounds of real-world medical images, whether OOD objects or inliers, are lesions. The difference between them is less than that between the foreground and background, possibly misleading the object queries to focus redundantly on the background. Thus, we propose a query-distribution (QD) loss to enforce clear boundaries between segmentation targets and other regions at the query level, improving the inlier segmentation and OOD indication. Our proposed framework is tested on two real-world segmentation tasks, i.e., segmentation of pancreatic and liver tumors, outperforming previous state-of-the-art algorithms by an average of 7.39% on AUROC, 14.69% on AUPR, and 13.79% on FPR95 for OOD localization. On the other hand, our framework improves the performance of inlier segmentation by an average of 5.27% DSC when compared with the leading baseline nnUNet.

关键词： cell microscopy Medical and biological vision

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Learning from Noisy Labels with Decoupled Meta Label Purifier

Learning from Noisy Labels with Decoupled Meta Label Purifie...

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

作者： Tu, Yuanpeng Zhang, Boshen Li, Yuxi Liu, Liang Li, Jian Wang, Yabiao Wang, Chengjie Zhao, Cai Rong Tongji Univ Dept Elect & Informat Engn Shanghai Peoples R China Tencent YouTu Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-learning based label correction strategy is widely adopted to tackle this problem via identifying and correcting potential noisy labels with the help of a small set of clean validation data. Although training with purified labels can effectively improve performance, solving the meta-learning problem inevitably involves a nested loop of bi-level optimization between model weights and hyper-parameters (i.e., label distribution). As compromise, previous methods resort to a coupled learning process with alternating update. In this paper, we empirically find such simultaneous optimization over both model weights and label distribution can not achieve an optimal routine, consequently limiting the representation ability of backbone and accuracy of corrected labels. From this observation, a novel multi-stage label purifier named DMLP is proposed. DMLP decouples the label correction process into label-free representation learning and a simple meta label purifier, In this way, DMLP can focus on extracting discriminative feature and label correction in two distinctive stages. DMLP is a plug-and-play label purifier, the purified labels can be directly reused in naive end-to-end network retraining or other robust learning methods, where state-of-the-art results are obtained on several synthetic and real-world noisy datasets, especially under high noise levels. Code is available at https://***/yuanpengtu/DMLP.

关键词： detection recognition: Categorization retrieval

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