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Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation

Semi-Supervised Semantic Depth Estimation using Symbiotic Tr...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rahman, Md Awsafur Fattah, Shaikh Anowarul BUET Dept EEE Dhaka Bangladesh

ISBN: (纸本)9798350318920;9798350318937

In computer vision, depth estimation is crucial for domains like robotics, autonomous vehicles, augmented reality, and virtual reality. Integrating semantics with depth enhances scene understanding through reciprocal information sharing. However, the scarcity of semantic information in datasets poses challenges. Existing convolutional approaches with limited local receptive fields hinder the full utilization of the symbiotic potential between depth and semantics. This paper introduces a dataset-invariant semi-supervised strategy to address the scarcity of semantic information. It proposes the Depth Semantics Symbiosis module, leveraging the Symbiotic Transformer for achieving comprehensive mutual awareness by information exchange within both local and global contexts. Additionally, a novel augmentation, NearFarMix is introduced to combat overfitting and compensate both depth-semantic tasks by strategically merging regions from two images, generating diverse and structurally consistent samples with enhanced control. Extensive experiments on NYU-Depth-V2 and KITTI datasets demonstrate the superiority of our proposed techniques in indoor and outdoor environments.

关键词： Algorithms Algorithms Algorithms and algorithms Computational photography formulations image and video synthesis Image recognition and understanding Machine learning architectures

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Restoring Degraded Old Films with Recursive Recurrent Transformer Networks

Restoring Degraded Old Films with Recursive Recurrent Transf...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Lin, Shan Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

There exists a large number of old films that have not only artistic value but also historical significance. However, due to the degradation of analogue medium over time, old films often suffer from various deteriorations that make it difficult to restore them with existing approaches. In this work, we proposed a novel framework called Recursive Recurrent Transformer Network (RRTN) which is specifically designed for restoring degraded old films. Our approach introduces several key advancements, including a more accurate film noise mask estimation method, the utilization of second-order grid propagation and flow-guided deformable alignment, and the incorporation of a recursive structure to further improve the removal of challenging film noise. Through qualitative and quantitative evaluations, our approach demonstrates superior performance compared to existing approaches, effectively improving the restoration for difficult film noises that cannot be perfectly handled by existing approaches. The code and model are available at https://***/ mountln/RRTN-oldfilm-restoration.

关键词： Algorithms Video recognition and understanding

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CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

CDDFuse: Correlation-Driven Dual-Branch Feature Decompositio...

引用

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

作者： Zhao, Zixiang Bai, Haowen Zhang, Jiangshe Zhang, Yulun Xu, Shuang Lin, Zudi Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen Peoples R China Northwestern Polytech Univ Xian Peoples R China Harvard Univ Cambridge England Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9798350301298

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features un-correlated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://***/Zhaozixiang1228/MMIF-CDDFuse.

关键词： Low-level vision

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Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning

Histopathology Whole Slide Image Analysis with Heterogeneous...

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

作者： Chan, Tsai Hor Cendra, Fernando Julio Ma, Lan Yin, Guosheng Yu, Lequan Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China TCL Corp Res Hong Kong Hong Kong Peoples R China Imperial Coll London Dept Math London England

ISBN: (纸本)9798350301298

Graph-based methods have been extensively applied to whole slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different entities. However, most of the existing methods focus on modeling WSIs with homogeneous graphs (e.g., with homogeneous node type). Despite their successes, these works are incapable of mining the complex structural relations between biological entities (e.g., the diverse interaction among different cell types) in the WSI. We propose a novel heterogeneous graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic similarity attribute to each edge. We then present a new heterogeneous-graph edge attribute transformer (HEAT) to take advantage of the edge and node heterogeneity during massage aggregating. Further, we design a new pseudo-label-based semantic-consistent pooling mechanism to obtain graph-level features, which can mitigate the over-parameterization issue of conventional cluster-based pooling. Additionally, observing the limitations of existing association-based localization methods, we propose a causal-driven approach attributing the contribution of each node to improve the interpretability of our framework. Extensive experiments on three public TCGA benchmark datasets demonstrate that our framework outperforms the state-of-the-art methods with considerable margins on various tasks. Our codes are available at https://***/HKU-MedAI/WSI-HGNN.

关键词： cell microscopy Medical and biological vision

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Bias and Diversity in Synthetic-based Face recognition

Bias and Diversity in Synthetic-based Face Recognition

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Huber, Marco Anh Thi Luu Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9798350318920;9798350318937

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias.

关键词： Algorithms Biometrics body pose face gesture

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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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MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic...

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

作者： Chen, Duowen Bai, Yunhao Shen, Wei Li, Qingli Yu, Lequan Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N-3 cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and N-3-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images. Code is avaiable at https://***/DeepMed-LabECNU/MagicNet.

关键词： cell microscopy Medical and biological vision

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OmniVec: Learning robust representations with cross modal sharing

OmniVec: Learning robust representations with cross modal sh...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Srivastava, Siddharth Sharma, Gaurav TensorTour Inc Seattle WA 98164 USA

ISBN: (纸本)9798350318920;9798350318937

Majority of research in learning based methods has been towards designing and training networks for specific tasks. However, many of the learning based tasks, across modalities, share commonalities and could be potentially tackled in a joint framework. We present an approach in such direction, to learn multiple tasks, in multiple modalities, with a unified architecture. The proposed network is composed of task specific encoders, a common trunk in the middle, followed by task specific prediction heads. We first pre-train it by self-supervised masked training, followed by sequential training for the different tasks. We train the network on all major modalities, e.g. visual, audio, text and 3D, and report results on 22 diverse and challenging public benchmarks. We demonstrate empirically that, using a joint network to train across modalities leads to meaningful information sharing and this allows us to achieve state-of-the-art results on most of the benchmarks. We also show generalization of the trained network on cross-modal tasks as well as unseen datasets and tasks.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Box-Level Active Detection

Box-Level Active Detection

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

作者： Lyu, Mengyao Zhou, Jundong Chen, Hui Huang, Yijie Yu, Dongdong Li, Yaqian Guo, Yandong Guo, Yuchen Xiang, Liuyu Ding, Guiguang Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China OPPO Res Inst Shenzhen Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchmarks conduct image-level evaluation, which is unrealistic in human workload estimation and biased towards crowded images. Furthermore, existing methods still perform image-level annotation, but equally scoring all targets within the same image incurs waste of budget and redundant labels. Having revealed above problems and limitations, we introduce a box-level active detection framework that controls a box-based budget per cycle, prioritizes informative targets and avoids redundancy for fair comparison and efficient application. Under the proposed box-level setting, we devise a novel pipeline, namely Complementary Pseudo Active Strategy (ComPAS). It exploits both human annotations and the model intelligence in a complementary fashion: an efficient input-end committee queries labels for informative objects only;meantime well-learned targets are identified by the model and compensated with pseudo-labels. ComPAS consistently outperforms 10 competitors under 4 settings in a unified codebase. With supervision from labeled data only, it achieves 100% supervised performance of VOC0712 with merely 19% box annotations. On the COCO dataset, it yields up to 4.3% mAP improvement over the second-best method. ComPAS also supports training with the unlabeled pool, where it surpasses 90% COCO supervised performance with 85% label reduction. Our source code is publicly available at https://***/lyumengyao/blad.

关键词： detection recognition: Categorization retrieval

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Domain Generalization by Rejecting Extreme Augmentations

Domain Generalization by Rejecting Extreme Augmentations

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Aminbeidokhti, Masih Pena, Fidel A. Guerrero Medeiros, Heitor Rapela Dubail, Thomas Granger, Eric Pedersoli, Marco ETS Montreal LIVIA Dept Syst Engn Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

Data augmentation is one of the most effective techniques for regularizing deep learning models and improving recognition performance in various tasks and domains. However, this holds for standard in-domain settings, in which the training and test data follow the same distribution. For the out-of-domain case, where the test data follow a different and unknown distribution, the best recipe for data augmentation is unclear. In this paper, we show that for out-of-domain and domain generalization settings, data augmentation can provide a conspicuous and robust improvement in performance. To do that, we propose a simple training procedure: (i) use uniform sampling on standard data augmentation transformations;(ii) increase the strength transformations to account for the higher data variance expected when working out-of-domain, and (iii) devise a new reward function to reject extreme transformations that can harm the training. With this procedure, our data augmentation scheme achieves a level of accuracy comparable to or better than state-of-the-art methods on benchmark domain generalization datasets. Code: https://***/Masseeh/DCAug

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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