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LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

LG-BPN: Local and Global Blind-Patch Network for Self-Superv...

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

作者： Wang, Zichun Fu, Ying Liu, Ji Zhang, Yulun Beijing Inst Technol Beijing Peoples R China Baidu Inc Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350301298

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blindspot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlation, or are limited by the destruction of fine textures for under-considering the correlation. In this paper, we present a novel method called LG-BPN for self-supervised real-world denoising, which takes the spatial correlation statistic into our network design for local detail restoration, and also brings the long-range dependencies modeling ability to previously CNN-based BSN methods. First, based on the correlation statistic, we propose a densely-sampled patch-masked convolution module. By taking more neighbor pixels with low noise correlation into account, we enable a denser local receptive field, preserving more useful information for enhanced fine structure recovery. Second, we propose a dilated Transformer block to allow distant context exploitation in BSN. This global perception addresses the intrinsic deficiency of BSN, whose receptive field is constrained by the blind spot requirement, which can not be fully resolved by the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit both the detailed structure and the global interaction in a blind manner. Extensive results on real-world datasets demonstrate the superior performance of our method. https://***/Wang-XIaoDingdd/LGBPN

关键词： Low-level vision

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Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

Open-World Multi-Task Control Through Goal-Aware Representat...

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

作者： Cai, Shaofei Wang, Zihao Ma, Xiaojian Liu, Anji Liang, Yitao Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China Univ Calif Los Angeles Dept Comp Sci Los Angeles CA USA Beijing Inst Gen Artificial Intelligence BIGAI Beijing Peoples R China

ISBN: (纸本)9798350301298

We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far;in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft. The code is released at https://***/CraftJarvis/MC-Controller.

关键词： Embodied vision: Active agents simulation

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Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training

Geometric Visual Similarity Learning in 3D Medical Image Sel...

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

作者： He, Yuting Yang, Guanyu Ge, Rongjun Chen, Yang Coatrieux, Jean-Louis Wang, Boyu Li, Shuo Southeast Univ Dhaka Bangladesh Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Univ Rennes 1 Rennes France Western Univ London England Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9798350301298

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions. To drive this paradigm, we further construct a novel geometric matching head, the Z-matching head, to collaboratively learn the global and local similarity of semantic regions, guiding the efficient representation learning for different scale-level inter-image semantic features. Our experiments demonstrate that the pre-training with our learning of inter-image similarity yields more powerful inner-scene, inter-scene, and global-local transferring ability on four challenging 3D medical image tasks. Our codes and pre-trained models will be publicly available.

关键词： cell microscopy Medical and biological vision

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Iterative Proposal Refinement forWeakly-Supervised Video Grounding

Iterative Proposal Refinement forWeakly-Supervised Video Gro...

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

作者： Cao, Meng Wei, Fangyun Xu, Can Geng, Xiubo Chen, Long Zhang, Can Zou, Yuexian Shen, Tao Jiang, Daxin Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Redmond WA 98052 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations. To date, most of the state-of-the-art WSVG methods follow a two-stage pipeline, i.e., firstly generating potential temporal proposals and then grounding with these proposal candidates. Despite the recent progress, existing proposal generation methods suffer from two drawbacks: 1) lack of explicit correspondence modeling;and 2) partial coverage of complex events. To this end, we propose a novel IteRative prOposal refiNement network (dubbed as IRON) to gradually distill the prior knowledge into each proposal and encourage proposals with more complete coverage. Specifically, we set up two lightweight distillation branches to uncover the cross-modal correspondence on both the semantic and conceptual levels. Then, an iterative Label Propagation (LP) strategy is devised to prevent the network from focusing excessively on the most discriminative events instead of the whole sentence content. Precisely, during each iteration, the proposal with the minimal distillation loss and its adjacent ones are regarded as the positive samples, which refines proposal confidence scores in a cascaded manner. Extensive experiments and ablation studies on two challenging WSVG datasets have attested to the effectiveness of our IRON. The code will be available at https://***/mengcaopku/IRON.

关键词： and reasoning language vision

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Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Learning with Noisy labels via Self-supervised Adversarial N...

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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 Zhang, Jiangning 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

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods. Code is available at https://***/yuanpengtu/SANM.

关键词： detection recognition: Categorization retrieval

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ReCo: Region-Controlled Text-to-Image Generation

ReCo: Region-Controlled Text-to-Image Generation

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

作者： Yang, Zhengyuan Wang, Jianfeng Gan, Zhe Li, Linjie Lin, Kevin Wu, Chenfei Duan, Nan Liu, Zicheng Liu, Ce Zeng, Michael Wang, Lijuan Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9798350301298

Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I models' inputs with an extra set of position tokens, which represent the quantized spatial coordinates. Each region is specified by four position tokens to represent the top-left and bottom-right corners, followed by an open-ended natural language regional description. Then, we fine-tune a pre-trained T2I model with such new input interface. Our model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set. Empirically, ReCo achieves better image quality than the T2I model strengthened by positional words (FID: 8.82 -> 7.36, SceneFID: 15.54 -> 6.51 on COCO), together with objects being more accurately placed, amounting to a 20.40% region classification accuracy improvement on COCO. Furthermore, we demonstrate that ReCo can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description. Human evaluation on PaintSkill shows that ReCo is +19.28% and +17.21% more accurate in generating images with correct object count and spatial relationship than the T2I model. Code is available at https://***/microsoft/ReCo.

关键词： language reasoning vision

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DartBlur: Privacy Preservation with Detection Artifact Suppression

DartBlur: Privacy Preservation with Detection Artifact Suppr...

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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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Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow

Unsupervised Cumulative Domain Adaptation for Foggy Scene Op...

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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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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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DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

DYNAFED: Tackling Client Data Heterogeneity with Global Dyna...

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

作者： Pi, Renjie Zhang, Weizhong Xie, Yueqi Gao, Jiahui Wang, Xiaoyu Kim, Sunghun Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Fudan Univ Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the server, such that the server has a global view of the entire data distribution. Unfortunately, this reduces to regular training, which compromises clients' privacy and conflicts with the purpose of FL. In this paper, we put forth an idea to collect and leverage global knowledge on the server without hindering data privacy. We unearth such knowledge from the dynamics of the global model's trajectory. Specifically, we first reserve a short trajectory of global model snapshots on the server. Then, we synthesize a small pseudo dataset such that the model trained on it mimics the dynamics of the reserved global model trajectory. Afterward, the synthesized data is used to help aggregate the deflected clients into the global model. We name our method DYNAFED, which enjoys the following advantages: 1) we do not rely on any external on-server dataset, which requires no additional cost for data collection;2) the pseudo data can be synthesized in early communication rounds, which enables DYNAFED to take effect early for boosting the convergence and stabilizing training;3) the pseudo data only needs to be synthesized once and can be directly utilized on the server to help aggregation in subsequent rounds. Experiments across extensive benchmarks are conducted to showcase the effectiveness of DYNAFED. We also provide insights and understanding of the underlying mechanism of our method.

关键词： accountability ethics in vision fairness privacy Transparency

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