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You Do Not Need Additional Priors or Regularizers in Retinex-based Low-light Image Enhancement

You Do Not Need Additional Priors or Regularizers in Retinex...

引用

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

作者： Fu, Huiyuan Zheng, Wenkai Meng, Xiangyu Wang, Xin Wang, Chuanming Ma, Huadong Beijing Univ Posts & Telecommun Beijing Peoples R China SUNY Stony Brook Stony Brook NY USA

ISBN: (纸本)9798350301298

Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed problem and there is no available ground truth. Previous works addressed this problem by imposing some additional priors or regularizers. However, finding an effective prior or regularizer that can be applied in various scenes is challenging, and the performance of the model suffers from too many additional constraints. We propose a contrastive learning method and a self-knowledge distillation method for Retinex decomposition that allow training our Retinex-based model without elaborate hand-crafted regularization functions. Rather than estimating reflectance and illuminance images and representing the final images as their element-wise products as in previous works, our regularizer-free Retinex decomposition and synthesis network (RFR) extracts reflectance and illuminance features and synthesizes them end-to-end. In addition, we propose a loss function for contrastive learning and a progressive learning strategy for self-knowledge distillation. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.

关键词： Low-level vision

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Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning

Class Balanced Adaptive Pseudo Labeling for Federated Semi-S...

引用

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

作者： Li, Ming Li, Qingli Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on federated semi-supervised learning (FSSL), assuming that few clients have fully labeled data (labeled clients) and the training datasets in other clients are fully unlabeled (unlabeled clients). Existing methods attempt to deal with the challenges caused by not independent and identically distributed data (Non-IID) setting. Though methods such as sub-consensus models have been proposed, they usually adopt standard pseudo labeling or consistency regularization on unlabeled clients which can be easily influenced by imbalanced class distribution. Thus, problems in FSSL are still yet to be solved. To seek for a fundamental solution to this problem, we present Class Balanced Adaptive Pseudo Labeling (CBAFed), to study FSSL from the perspective of pseudo labeling. In CBAFed, the first key element is a fixed pseudo labeling strategy to handle the catastrophic forgetting problem, where we keep a fixed set by letting pass information of unlabeled data at the beginning of the unlabeled client training in each communication round. The second key element is that we design class balanced adaptive thresholds via considering the empirical distribution of all training data in local clients, to encourage a balanced training process. To make the model reach a better optimum, we further propose a residual weight connection in local supervised training and global model aggregation. Extensive experiments on five datasets demonstrate the superiority of CBAFed. Code will be available at https://***/minglllli/CBAFed.

关键词： vision applications and systems

来源：评论

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Neuralizer: General Neuroimage Analysis without Re-Training

Neuralizer: General Neuroimage Analysis without Re-Training

引用

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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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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Boost vision Transformer with GPU-Friendly Sparsity and Quantization

Boost Vision Transformer with GPU-Friendly Sparsity and Quan...

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

作者： Yu, Chong Chen, Tao Gan, Zhongxue Fan, Jiayuan Fudan Univ Acad Engn & Technol Shanghai Peoples R China NVIDIA Corp Taipei Taiwan Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The transformer extends its success from the language to the vision domain. Because of the stacked self-attention and cross-attention blocks, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models 6.4-12.7x on model size and 30.3-62x on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by 1.39-1.79x and 3.22-3.43x of latency and throughput on A100 GPU, and 1.57-1.69x and 2.11-2.51x improvement of latency and throughput on AGX Orin.

关键词： Efficient and scalable vision

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Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging

Learning Accurate 3D Shape Based on Stereo Polarimetric Imag...

引用

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

作者： Huang, Tianyu Li, Haoang He, Kejing Sui, Congying Li, Bin Liu, Yun-Hui Chinese Univ Hong Kong Hong Kong Peoples R China Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Shape from Polarization (SfP) aims to recover surface normal using the polarization cues of light. The accuracy of existing SfP methods is affected by two main problems. First, the ambiguity of polarization cues partially results in false normal estimation. Second, the widely-used assumption about orthographic projection is too ideal. To solve these problems, we propose the first approach that combines deep learning and stereo polarization information to recover not only normal but also disparity. Specifically, for the ambiguity problem, we design a Shape Consistency-based Mask Prediction (SCMP) module. It exploits the inherent consistency between normal and disparity to identify the areas with false normal estimation. We replace the unreliable features enclosed by these areas with new features extracted by global attention mechanism. As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy. This strategy is based on the unique pixel-viewing direction encoding, and thus enables our neural network to handle the non-orthographic projection. In addition, we establish a real-world stereo SfP dataset that contains various object categories and illumination conditions. Experiments showed that compared with existing SfP methods, our approach is more accurate. Moreover, our approach shows higher robustness to light variation.

关键词： Physics-based vision and shape-from-X

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Ingredient-oriented Multi-Degradation Learning for Image Restoration

Ingredient-oriented Multi-Degradation Learning for Image Res...

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

作者： Zhang, Jinghao Huang, Jie Yao, Mingde Yang, Zizheng Yu, Hu Zhou, Man Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamental ingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via an ingredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.

关键词： Low-level vision

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Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

Interventional Bag Multi-Instance Learning On Whole-Slide Pa...

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

作者： Lin, Tiancheng Yu, Zhimiao Hu, Hongyu Xu, Yi Chen, Chang Wen Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into capturing spurious correlations between bags and labels. This deficiency is a confounder that limits the performance of existing MIL methods. In this paper, we propose a novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve deconfounded bag-level prediction. Unlike traditional likelihood-based strategies, the proposed scheme is based on the backdoor adjustment to achieve the interventional training, thus is capable of suppressing the bias caused by the bag contextual prior. Note that the principle of IBMIL is orthogonal to existing bag MIL methods. Therefore, IBMIL is able to bring consistent performance boosting to existing schemes, achieving new state-of-the-art performance. Code is available at https://***/HHHedo/IBMIL.

关键词： cell microscopy Medical and biological vision

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Masked Autoencoders Enable Efficient Knowledge Distillers

Masked Autoencoders Enable Efficient Knowledge Distillers

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

作者： Bai, Yutong Wang, Zeyu Xiao, Junfei Wei, Chen Wang, Huiyu Yuille, Alan Zhou, Yuyin Xie, Cihang Johns Hopkins Univ Baltimore MD 21218 USA Univ Calif Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9798350301298

This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel reconstruction loss on masked inputs, we minimize the distance between the intermediate feature map of the teacher model and that of the student model. This design leads to a computationally efficient knowledge distillation framework, given 1) only a small visible subset of patches is used, and 2) the (cumbersome) teacher model only needs to be partially executed, i.e., forward propagate inputs through the first few layers, for obtaining intermediate feature maps. Compared to directly distilling fine-tuned models, distilling pre-trained models substantially improves downstream performance. For example, by distilling the knowledge from an MAE pre-trained ViT-L into a ViT-B, our method achieves 84.0% ImageNet top-1 accuracy, outperforming the baseline of directly distilling a fine-tuned ViT-L by 1.2%. More intriguingly, our method can robustly distill knowledge from teacher models even with extremely high masking ratios: e.g., with 95% masking ratio where merely TEN patches are visible during distillation, our ViT-B competitively attains a top-1 ImageNet accuracy of 83.6%;surprisingly, it can still secure 82.4% top-1 ImageNet accuracy by aggressively training with just FOUR visible patches (98% masking ratio). The code and models are publicly available at https://***/UCSC-VLAA/DMAE.

关键词： Efficient and scalable vision

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CoWs on PASTURE: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation

CoWs on PASTURE: Baselines and Benchmarks for Language-Drive...

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

作者： Gadre, Sarnir Yitzhak Wortsman, Mitchell Ilharco, Gabriel Schmidt, Ludwig Song, Shuran Columbia Univ New York NY 10027 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350301298

For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inference). We explore these capabilities in a unified setting: language-driven zero-shot object navigation (L-ZSON). Inspired by the recent success of open-vocabulary models for image classification, we investigate a straightforward framework, CLIP on Wheels (CoW), to adapt open-vocabulary models to this task without fine-tuning. To better evaluate L-ZSON, we introduce the PASTURE benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects. We conduct an in-depth empirical study by directly deploying 22 CoW baselines across HABITAT, ROBOTHOR, and PASTURE. In total, we evaluate over 90k navigation episodes and find that (1) CoW baselines often struggle to leverage language descriptions but are proficient at finding uncommon objects. (2) A simple CoW, with CLIP-based object localization and classical exploration-and no additional training-matches the navigation efficiency of a state-of-the-art ZSON method trained for 500M steps on HABITAT MP3D data. This same CoW provides a 15.6 percentage point improvement in success over a state-of-the-art ROBOTHOR ZSON model.(1)

关键词： Embodied vision: Active agents simulation

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