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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

Bidirectional Copy-Paste for Semi-Supervised Medical Image S...

引用

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

作者： Bai, Yunhao Chen, Duowen Li, Qingli Shen, Wei 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

ISBN: (纸本)9798350301298

In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is avaiable at https://***/DeepMed-Lab-ECNU/BCP.

关键词： cell microscopy Medical and biological vision

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DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

DINN360: Deformable Invertible Neural Network for Latitude-a...

引用

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

作者： Guo, Yichen Xu, Mai Jiang, Lai Sigal, Leonid Chen, Yunjin Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (纸本)9798350301298

With the rapid development of virtual reality, 360 degrees images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360 degrees images. To alleviate this issue, we propose the first attempt at 360 degrees image rescaling, which refers to downscaling a 360 degrees image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360 degrees image given the LR variant. Specifically, we first analyze two 360 degrees image datasets and observe several findings that characterize how 360 degrees images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360 degrees image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2x, 4x and 8x 360 degrees image rescaling.

关键词： Low-level vision

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Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow

Unsupervised Cumulative Domain Adaptation for Foggy Scene Op...

引用

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

作者： Zhou, Hanyu Chang, Yi Yan, Wending Yan, Luxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Hubei Peoples R China Huawei Int Co Ltd Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpectedly neglect the synthetic-to-real domain gap, and thus are erroneous when applied to real-world scenes. To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation. Specifically, we discover that depth is a key ingredient to influence the optical flow: the deeper depth, the inferior optical flow, which motivates us to design a depth-association motion adaptation module to bridge the clean-to-foggy domain gap. Moreover, we figure out that the cost volume correlation shares similar distribution of the synthetic and real foggy images, which enlightens us to devise a correlation-alignment motion adaptation module to distill motion knowledge of the synthetic foggy domain to the real foggy domain. Note that synthetic fog is designed as the intermediate domain. Under this unified framework, the proposed cumulative adaptation progressively transfers knowledge from clean scenes to real foggy scenes. Extensive experiments have been performed to verify the superiority of the proposed method.

关键词： Low-level vision

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DartBlur: Privacy Preservation with Detection Artifact Suppression

DartBlur: Privacy Preservation with Detection Artifact Suppr...

引用

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

作者： Jiang, Baowei Bai, Bing Lin, Haozhe Wang, Yu Guo, Yuchen Fang, Lu Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Nowadays, privacy issue has become a top priority when training AI algorithms. Machine learning algorithms are expected to benefit our daily life, while personal information must also be carefully protected from exposure. Facial information is particularly sensitive in this regard. Multiple datasets containing facial information have been taken offline, and the community is actively seeking solutions to remedy the privacy issues. Existing methods for privacy preservation can be divided into blur-based and face replacement-based methods. Owing to the advantages of review convenience and good accessibility, blur-based based methods have become a dominant choice in practice. However, blur-based methods would inevitably introduce training artifacts harmful to the performance of downstream tasks. In this paper, we propose a novel De-artifact Blurring (DartBlur) privacy-preserving method, which capitalizes on a DNN architecture to generate blurred faces. DartBlur can effectively hide facial privacy information while detection artifacts are simultaneously suppressed. We have designed four training objectives that particularly aim to improve review convenience and maximize detection artifact suppression. We associate the algorithm with an adversarial training strategy with a second-order optimization pipeline. Experimental results demonstrate that DartBlur outperforms the existing face-replacement method from both perspectives of review convenience and accessibility, and also shows an exclusive advantage in suppressing the training artifact compared to traditional blur-based methods. Our implementation is available at https://***/JaNg2333/DartBlur.

关键词： accountability ethics in vision fairness privacy Transparency

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Real-time Controllable Denoising for Image and Video

Real-time Controllable Denoising for Image and Video

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

作者： Zhang, Zhaoyang Jiang, Yitong Shao, Wenqi Wang, Xiaogang Luo, Ping Lin, Kaimo Gu, Jinwei Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China SenseBrain San Jose CA USA

ISBN: (纸本)9798350301298

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.

关键词： Low-level vision

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NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

引用

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

作者： Hsu, Joy Mao, Jiayuan Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA USA

ISBN: (纸本)9798350301298

Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D grounded language. Hence, essential desiderata for models are to be data-efficient, generalize to different data distributions and tasks with unseen semantic forms, as well as ground complex language semantics (e.g., view-point anchoring and multi-object reference). To address these challenges, we propose NS3D, a neuro-symbolic framework for 3D grounding. NS3D translates language into programs with hierarchical structures by leveraging large language-to-code models. Different functional modules in the programs are implemented as neural networks. Notably, NS3D extends prior neuro-symbolic visual reasoning methods by introducing functional modules that effectively reason about high-arity relations (i.e., relations among more than two objects), key in disambiguating objects in complex 3D scenes. Modular and compositional architecture enables NS3D to achieve state-of-the-art results on the ReferIt3D view-dependence task, a 3D referring expression comprehension benchmark. Importantly, NS3D shows significantly improved performance on settings of data-efficiency and generalization, and demonstrate zero-shot transfer to an unseen 3D question-answering task.

关键词： language reasoning vision

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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

作者： Zhang, Xi Zhang, Feifei Xu, Changsheng 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 Peng Cheng Lab Shenzhen Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China

ISBN: (纸本)9798350301298

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

关键词： language reasoning vision

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RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion

RODIN: A Generative Model for Sculpting 3D Digital Avatars U...

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

作者： Wang, Tengfei Zhang, Bo Zhang, Ting Gu, Shuyang Bao, Jianmin Baltrusaitis, Tadas Shen, Jingjing Chen, Dong Wen, Fang Chen, Qifeng Guo, Baining HKUST Hong Kong Peoples R China Microsoft Res Beijing Peoples R China Intern Microsoft Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

This paper presents a 3D diffusion model that automatically generates 3D digital avatars represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is that the memory and processing costs are prohibitive for producing high-quality results with rich details. To tackle this problem, we propose the roll-out diffusion network (RODIN), which takes a 3D NeRF model represented as multiple 2D feature maps and rolls out them onto a single 2D feature plane within which we perform 3D-aware diffusion. The RODIN model brings much-needed computational efficiency while preserving the integrity of 3D diffusion by using 3D-aware convolution that attends to projected features in the 2D plane according to their original relationships in 3D. We also use latent conditioning to orchestrate the feature generation with global coherence, leading to high-fidelity avatars and enabling semantic editing based on text prompts. Finally, we use hierarchical synthesis to further enhance details. The 3D avatars generated by our model compare favorably with those produced by existing techniques. We can generate highly detailed avatars with realistic hairstyles and facial hair. We also demonstrate 3D avatar generation from image or text, as well as text-guided editability.

关键词： vision + graphics

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Learning Situation Hyper-Graphs for Video Question Answering

Learning Situation Hyper-Graphs for Video Question Answering

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

作者： Khan, Aisha Urooj Kuehne, Hilde Wu, Bo Chheu, Kim Bousselham, Walid Gan, Chuang Lobo, Niels Shah, Mubarak Univ Cent Florida CRCV Orlando FL 32816 USA MIT IBM Watson AI Lab Cambridge MA USA Mayo Clin Phoenix AZ 85054 USA Goethe Univ Frankfurt Germany Frankfurt Germany Western Michigan Univ Kalamazoo MI USA UMass Amherst Amherst MA USA

ISBN: (纸本)9798350301298

Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks.

关键词： and reasoning language vision

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Twin Contrastive Learning with Noisy Labels

Twin Contrastive Learning with Noisy Labels

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

作者： Huang, Zhizhong Zhang, Junping Shan, Hongming Fudan Univ Shanghai Key Lab Intelligent Informat Proc Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ MOE Frontiers Ctr Brain Sci Shanghai 200433 Peoples R China Shanghai Ctr Brain Sci & Brain Inspired Technol Shanghai 200031 Peoples R China

ISBN: (纸本)9798350301298

Learning from noisy data is a challenging task that significantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification. Specifically, we construct a Gaussian mixture model (GMM) over the representations by injecting the supervised model predictions into GMM to link label-free latent variables in GMM with label-noisy annotations. Then, TCL detects the examples with wrong labels as the out-of-distribution examples by another two-component GMM, taking into account the data distribution. We further propose a cross-supervision with an entropy regularization loss that bootstraps the true targets from model predictions to handle the noisy labels. As a result, TCL can learn discriminative representations aligned with estimated labels through mixup and contrastive learning. Extensive experimental results on several standard benchmarks and real-world datasets demonstrate the superior performance of TCL. In particular, TCL achieves 7.5% improvements on CIFAR-10 with 90% noisy label-an extremely noisy scenario. The source code is available at https://***/Hzzone/TCL.

关键词： detection recognition: Categorization retrieval

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