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Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera

Generating Aligned Pseudo-Supervision from Non-Aligned Data ...

引用

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

作者： Feng, Ruicheng Li, Chongyi Chen, Huaijin Li, Shuai Gu, Jinwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore SenseBrain Technol San Jose CA USA Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data collection - capturing two images of the same scene with one UDC and one standard camera. The key idea is to "copy" details from a high-quality reference image and "paste" them on the UDC image. While being able to generate real training pairs, this setting is susceptible to spatial misalignment due to perspective and depth of field changes. The problem is further compounded by the large domain discrepancy between the UDC and normal images, which is unique to UDC restoration. In this paper, we mitigate the non-trivial domain discrepancy and spatial misalignment through a novel Transformer-based framework that generates well-aligned yet high-quality target data for the corresponding UDC input. This is made possible through two carefully designed components, namely, the Domain Alignment Module (DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate discovery of correspondence between the UDC and normal views. Extensive experiments show that high-quality and well-aligned pseudo UDC training pairs are beneficial for training a robust restoration network. Code and the dataset are available at https://github. com/ jnjaby/AlignFormer.

关键词： Low-level vision

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Finetune like you pretrain: Improved finetuning of zero-shot vision models

Finetune like you pretrain: Improved finetuning of zero-shot...

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

作者： Goyal, Sachin Kumar, Ananya Garg, Sankalp Kolter, Zico Raghunathan, Aditi Carnegie Mellon Univ Pittsburgh PA 15213 USA Stanford Univ Stanford CA 94305 USA Bosch Ctr AI Renningen Germany

ISBN: (纸本)9798350301298

Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works (Kumar et al., 2022;Wortsman et al., 2021) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data. In this work, we show that a natural and simple approach of mimicking contrastive pretraining consistently outperforms alternative finetuning approaches. Specifically, we cast downstream class labels as text prompts and continue optimizing the contrastive loss between image embeddings and class-descriptive prompt embeddings (contrastive finetuning). Our method consistently outperforms baselines across 7 distribution shift, 6 transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our proposed approach FLYP outperforms the top of the leaderboard by 2.3% ID and 2.7% OOD, giving the highest reported accuracy. Averaged across 7 OOD datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of 4.2% OOD over standard finetuning and outperforms current state-of-the-art (LP-FT) by more than 1% both ID and OOD. Similarly, on 3 few-shot learning benchmarks, FLYP gives gains up to 4.6% over standard finetuning and 4.4% over the state-of-the-art. Thus we establish our proposed method of contrastive finetuning as a simple and intuitive state-of-the-art for supervised finetuning of image-text models like CLIP. Code is available at https://***/locuslab/FLYP.

关键词： continual low-shot meta or long-tail learning Transfer

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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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Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification

Task-specific Fine-tuning via Variational Information Bottle...

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

作者： Li, Honglin Zhu, Chenglu Zhang, Yunlong Sun, Yuxuan Shui, Zhongyi Kuang, Wenwei Zheng, Sunyi Yang, Lin Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and generalization problems due to high computational costs and limited supervision of Gigapixel WSIs. To deal with the computation problem, previous methods utilize a frozen model pretrained from ImageNet to obtain representations, however, it may lose key information owing to the large domain gap and hinder the generalization ability without image-level training-time augmentation. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the downstream task-specific features via partial label tuning are not explored. To alleviate this problem, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. We evaluate the method on five pathological WSI datasets on various WSI heads. The experimental results show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://***/invoker-LL/WSI-finetuning.

关键词： cell microscopy Medical and biological vision

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Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising

Structure Aggregation for Cross-Spectral Stereo Image Guided...

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

作者： Sheng, Zehua Yu, Zhu Liu, Xiongwei Cao, Si-Yuan Liu, Yuqi Shen, Hui-Liang Zhang, Huaqi Zhejiang Univ Hangzhou Peoples R China Vivo Mobile Commun Co Ltd Dongguan Peoples R China

ISBN: (纸本)9798350301298

To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guided denoising methods basically require the input images to be well-aligned, a more common way to capture the paired noisy target and guidance images is to exploit a stereo camera system. However, current studies on cross-spectral stereo matching cannot fully guarantee the pixel-level registration accuracy, and rarely consider the case of noise contamination. In this work, for the first time, we propose a guided denoising framework for cross-spectral stereo images. Instead of aligning the input images via conventional stereo matching, we aggregate structures from the guidance image to estimate a clean structure map for the noisy target image, which is then used to regress the final denoising result with a spatially variant linear representation model. Based on this, we design a neural network, called as SANet, to complete the entire guided denoising process. Experimental results show that, our SANet can effectively transfer structures from an unaligned guidance image to the restoration result, and outperforms state-of-the-art denoisers on various stereo image datasets. Besides, our structure aggregation strategy also shows its potential to handle other unaligned guided restoration tasks such as super-resolution and deblurring. The source code is available at https://***/lustrouselixir/SANet.

关键词： Low-level vision

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Q-DETR: An Efficient Low-Bit Quantized Detection Transformer

Q-DETR: An Efficient Low-Bit Quantized Detection Transformer

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

作者： Xu, Sheng Li, Yanjing Lin, Mingbao Gao, Peng Guo, Guodong Lu, Jinhu Zhang, Baochang Beihang Univ Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Tencent Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Ubiquitous Co UNIUBI Res Shanghai Peoples R China

ISBN: (纸本)9798350301298

The recent detection transformer (DETR) has advanced object detection, but its application on resource-constrained devices requires massive computation and memory resources. Quantization stands out as a solution by representing the network in low-bit parameters and operations. However, there is a significant performance drop when performing low-bit quantized DETR (Q-DETR) with existing quantization methods. We find that the bottle-necks of Q-DETR come from the query information distortion through our empirical analyses. This paper addresses this problem based on a distribution rectification distillation (DRD). We formulate our DRD as a bi-level optimization problem, which can be derived by generalizing the information bottleneck (IB) principle to the learning of Q-DETR. At the inner level, we conduct a distribution alignment for the queries to maximize the self-information entropy. At the upper level, we introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy. Extensive experimental results show that our method performs much better than prior arts. For example, the 4-bit Q-DETR can theoretically accelerate DETR with ResNet-50 backbone by 6.6x and achieve 39.4% AP, with only 2.6% performance gaps than its real-valued counterpart on the COCO dataset

关键词： detection recognition: Categorization retrieval

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SmartAssign: Learning A Smart Knowledge Assignment Strategy for Deraining and Desnowing

SmartAssign: Learning A Smart Knowledge Assignment Strategy ...

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

作者： Wang, Yinglong Mai, Chao Liu, Jianzhuang Huawei Noahs Ark Lab Montreal PQ Canada Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

Existing methods mainly handle single weather types. However, the connections of different weather conditions at deep representation level are usually ignored. These connections, if used properly, can generate complementary representations for each other to make up insufficient training data, obtaining positive performance gains and better generalization. In this paper, we focus on the very correlated rain and snow to explore their connections at deep representation level. Because sub-optimal connections may cause negative effect, another issue is that if rain and snow are handled in a multi-task learning way, how to find an optimal connection strategy to simultaneously improve deraining and desnowing performance. To build desired connection, we propose a smart knowledge assignment strategy, called SmartAssign, to optimally assign the knowledge learned from both tasks to a specific one. In order to further enhance the accuracy of knowledge assignment, we propose a novel knowledge contrast mechanism, so that the knowledge assigned to different tasks preserves better uniqueness. The inherited inductive biases usually limit the modelling ability of CNNs, we introduce a novel transformer block to constitute the backbone of our network to effectively combine long-range context dependency and local image details. Extensive experiments on seven benchmark datasets verify that proposed SmartAssign explores effective connection between rain and snow, and improves the performances of both deraining and desnowing apparently. The implementation code will be available at https:// ***/ mindspore/models/tree/master/ research/cv/SmartAssign.

关键词： Low-level vision

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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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TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification

TranSG: Transformer-Based Skeleton Graph Prototype Contrasti...

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

作者： Rao, Haocong Miao, Chunyan Nanyang Technol Univ LILY Res Ctr Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9798350301298

Person re-identification (re-ID) via 3D skeleton data is an emerging topic with prominent advantages. Existing methods usually design skeleton descriptors with raw body joints or perform skeleton sequence representation learning. However, they typically cannot concurrently model different body-component relations, and rarely explore useful semantics from fine-grained representations of body joints. In this paper, we propose a generic Transformer-based Skeleton Graph prototype contrastive learning (TranSG) approach with structure-trajectory prompted reconstruction to fully capture skeletal relations and valuable spatial-temporal semantics from skeleton graphs for person re-ID. Specifically, we first devise the Skeleton Graph Transformer (SGT) to simultaneously learn body and motion relations within skeleton graphs, so as to aggregate key correlative node features into graph representations. Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the most typical graph features (graph prototypes) of each identity, and contrast the inherent similarity between graph representations and different prototypes from both skeleton and sequence levels to learn discriminative graph representations. Last, a graph Structure-Trajectory Prompted Reconstruction (STPR) mechanism is proposed to exploit the spatial and temporal contexts of graph nodes to prompt skeleton graph reconstruction, which facilitates capturing more valuable patterns and graph semantics for person re-ID. Empirical evaluations demonstrate that TranSG significantly outperforms existing state-of-the-art methods. We further show its generality under different graph modeling, RGB-estimated skeletons, and unsupervised scenarios. Our codes are available at https://***/KaliHac/TranSG.

关键词： detection recognition: Categorization retrieval

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PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes

PlaneDepth: Self-supervised Depth Estimation via Orthogonal ...

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

作者： Wang, Ruoyu Yu, Zehao Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Univ Tubingen Tubingen Germany Shanghai Engn Res Ctr Intelligent Vision & Imagin Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom I Shanghai Peoples R China

ISBN: (纸本)9798350301298

Multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MDE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the PlaneDepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://***/svip-lab/PlaneDepth.

关键词： 3D from multi-view and sensors

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