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All-In-One Image Restoration for Unknown Corruption

All-In-One Image Restoration for Unknown Corruption

引用

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

作者： Li, Boyun Liu, Xiao Hu, Peng Wu, Zhongqin Lv, Jiancheng Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China TAL Educ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://***/XLearning-SCU/2022-cvpr-AirNet.

关键词： Degradation Photography computer vision Codes Image restoration pattern recognition

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Neural Collaborative Graph Machines for Table Structure recognition

Neural Collaborative Graph Machines for Table Structure Reco...

引用

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

作者： Liu, Hao Li, Xin Liu, Bing Jiang, Deqiang Liu, Yinsong Ren, Bo Tencent YouTu Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually reasoning in terms of multiple modalities can be appropriate for all varieties of table structures with great diversity. Instead, different modalities are expected to collaborate with each other in different patterns for different table cases. In the community, the importance of intra-inter modality interactions for table structure reasoning is still unexplored. In this paper, we define it as heterogeneous table structure recognition (Hetero-TSR) problem. With the aim of filling this gap, we present a novel Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative blocks, which alternatively extracts intra-modality context and models inter-modality interactions in a hierarchical way. It can represent the introinter modality relationships of tabular elements more robustly, which significantly improves the recognition performance. We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases. Experimental results on benchmarks demonstrate our proposed NCGM achieves state-of-the-art performance and beats other contemporary methods by a large margin especially under challenging scenarios.

关键词： Visualization computer vision Text analysis Collaboration Benchmark testing Cognition pattern recognition

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Moving Window Regression: A Novel Approach to Ordinal Regression

Moving Window Regression: A Novel Approach to Ordinal Regres...

引用

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

作者： Shin, Nyeong-Ho Lee, Seon-Ho Kim, Chang-Su Korea Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (p -rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (p-regressors) to predict p-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the p-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-theart performances on various benchmark datasets for facial age estimation and historical color image classification.

关键词： computer vision Codes Estimation Color Benchmark testing Prediction algorithms Classification algorithms

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RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs

RestoreFormer: High-Quality Blind Face Restoration from Unde...

引用

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

作者： Wang, Zhouxia Zhang, Jiawei Chen, Runjian Wang, Wenping Luo, Ping Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality. Code is available at https://***/wzhouxiff/***.

关键词： Degradation Visualization computer vision Dictionaries Face recognition Transformers Image restoration

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Semantic-aligned Fusion Transformer for One-shot Object Detection

Semantic-aligned Fusion Transformer for One-shot Object Dete...

引用

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

作者： Zhao, Yizhou Guo, Xun Lu, Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft Res Asia Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their performances are often unsatisfactory. In this paper, we attribute this to inappropriate correlation methods that misalign query-support semantics by overlooking spatial structures and scale variances. Upon analysis, we leverage the attention mechanism and propose a simple but effective architecture named Semantic-aligned Fusion Transformer (SaFT) to resolve these issues. Specifically, we equip SaFT with a vertical fusion module (VFM) for cross-scale semantic enhancement and a horizontal fusion module (HFM) for cross-sample feature fusion. Together, they broaden the vision for each feature point from the support to a whole augmented feature pyramid from the query, facilitating semantic-aligned associations. Extensive experiments on multiple benchmarks demonstrate the superiority of our framework. Without fine-tuning on novel classes, it brings significant performance gains to one-stage baselines, lifting state-of-the-art results to a higher level.

关键词： computer vision Correlation Semantics Object detection computer architecture Benchmark testing Transformers

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Visual Acoustic Matching

Visual Acoustic Matching

引用

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

作者： Chen, Changan Gao, Ruohan Calamia, Paul Grauman, Kristen Univ Texas Austin Austin TX 78712 USA Stanford Univ Stanford CA 94305 USA Meta Real Labs Res New York NY USA Meta AI New York NY 10003 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment. Given an image of the target environment and a waveform for the source audio, the goal is to re-synthesize the audio to match the target room acoustics as suggested by its visible geometry and materials. To address this novel task, we propose a cross-modal transformer model that uses audio-visual attention to inject visual properties into the audio and generate realistic audio output. In addition, we devise a self-supervised training objective that can learn acoustic matching from in-the-wild Web videos, despite their lack of acoustically mismatched audio. We demonstrate that our approach successfully translates human speech to a variety of real-world environments depicted in images, outperforming both traditional acoustic matching and more heavily supervised baselines.

关键词： Training Geometry Visualization computer vision Computational modeling Transformers Acoustics

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PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors

PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervi...

引用

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

作者： Revaud, Jerome Leroy, Vincent Weinzaepfel, Philippe Chidlovskii, Boris NAVER LABS Europe Meylan France

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing approaches for learning local image descriptors have shown remarkable achievements in a wide range of geometric tasks. However, most of them require per-pixel correspondence-level supervision, which is difficult to acquire at scale and in high quality. In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision. Given two images depicting the same scene, we extract pixel descriptors and build a correlation volume. The first prior enforces the local consistency of matches in this volume via a pyramidal structure iteratively constructed using a non-parametric module. The second prior exploits the fact that each descriptor should match with at most one descriptor from the other image. We combine our unsupervised loss with a standard self-supervised loss trained from synthetic image augmentations. Feature descriptors learned by the proposed approach outperform their fully- and self-supervised counterparts on various geometric benchmarks such as visual localization and image matching, achieving state-of-the-art performance. Project webpage: https://***/research/3d-vision/pump.

关键词： Location awareness Training Visualization Image matching Pipelines Training data pattern recognition

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Enhancing Adversarial Robustness for Deep Metric Learning

Enhancing Adversarial Robustness for Deep Metric Learning

引用

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

作者： Zhou, Mo Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Owing to security implications of adversarial vulnerability, adversarial robustness of deep metric learning models has to be improved. In order to avoid model collapse due to excessively hard examples, the existing defenses dismiss the min-max adversarial training, but instead learn from a weak adversary inefficiently. Conversely, we propose Hardness Manipulation to efficiently perturb the training triplet till a specified level of hardness for adversarial training, according to a harder benign triplet or a pseudo-hardness function. It is flexible since regular training and min-max adversarial training are its boundary cases. Besides, Gradual Adversary, a family of pseudo-hardness functions is proposed to gradually increase the specified hardness level during training for a better balance between performance and robustness. Additionally, an Intra-Class Structure loss term among benign and adversarial examples further improves model robustness and efficiency. Comprehensive experimental results suggest that the proposed method, although simple in its form, overwhelmingly outperforms the state-of-the-art defenses in terms of robustness, training efficiency, as well as performance on benign examples.

关键词： Training Measurement computer vision Computational modeling Robustness pattern recognition Security

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Searching the Deployable Convolution Neural Networks for GPUs

Searching the Deployable Convolution Neural Networks for GPU...

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

作者： Wang, Linnan Yu, Chenhan Salian, Satish Kierat, Slawomir Migacz, Szymon Florea, Alex Fit NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy. Since we target GPU, we name the NAS optimized models as GPUNet, which establishes a new SOTA Pareto frontier in inference latency and accuracy. Within 1ms, GPUNet is 2x faster than EfficientNet-X and FBNetV3 with even better accuracy. We also validate GPUNet on detection tasks, and GPUNet consistently outperforms EfficientNet-X and FBNetV3 on COCO detection tasks in both latency and accuracy. All of these data validate that our NAS system is effective and generic to handle different design tasks. With this NAS system, we expand GPUNet to cover a wide range of latency targets such that DL practitioners can deploy our models directly in different scenarios.

关键词： computer vision Convolution Computational modeling Neural networks Graphics processing units Production pattern recognition

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PubTables-1M: Towards comprehensive table extraction from unstructured documents

PubTables-1M: Towards comprehensive table extraction from un...

引用

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

作者： Smock, Brandon Pesala, Rohith Abraham, Robin Microsoft Redmond WA 98052 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks. Data and code will be released at https://***/microsoft/table-transformer.

关键词： Training computer vision Object detection Machine learning Transformers Functional analysis pattern recognition

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