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Graph Neural Networks for End-to-End Information Extraction from Handwritten Documents

Graph Neural Networks for End-to-End Information Extraction ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Khanfir, Yessine Dhiaf, Marwa Ghodhbani, Emna Rouhou, Ahmed Cheikh Kessentini, Yousri InstaDeep Tunis Tunisia Digital Res Ctr Sfax Tunis Tunisia SMaRTS Lab Signals Syst Artificial Intelligence & Tunis Tunisia

ISBN: (纸本)9798350318920;9798350318937

Automating Information Extraction (IE) from handwritten documents is a challenging task due to the wide variety of handwriting styles, the presence of noise, and the lack of labeled data. In this work, we propose an end-to-end encoder-decoder model, that incorporates transformers and Graph Convolutional Networks (GCN), to jointly perform Handwritten Text recognition (HTR) and Named Entity recognition (NER). The proposed architecture is mainly composed of two parts: a Sparse Graph Transformer Encoder (SGTE), to capture efficient representations of input text images while controlling the propagation of information through the model. The SGTE is followed by a transformer decoder enhanced with a GCN that combines the outputs of the last SGTE layer and the Multi-Head Attention (MHA) block to reinforce the alignment of visual features to characters and Named Entity (NE) tags, resulting in more robust learned representations. The proposed model shows promising results and achieves state-of-the-art performance on the IAM dataset, and in the ICDAR 2017 Information Extraction competition using the Esposalles database.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Ingredient-oriented Multi-Degradation Learning for Image Restoration

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

引用

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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Decoupled Multimodal Distilling for Emotion recognition

Decoupled Multimodal Distilling for Emotion Recognition

引用

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

作者： Li, Yong Wang, Yuanzhi Cui, Zhen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst High Dimens In Minist EducPCA Lab Nanjing Peoples R China

ISBN: (纸本)9798350301298

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are released at https://***/mdswyz/DMD.

关键词： body gesture Humans: Face movement pose

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Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

Efficient Frequency Domain-based Transformers for High-Quali...

引用

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

作者： Kong, Lingshun Dong, Jiangxin Ge, Jianjun Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

关键词： Low-level vision

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Are We Ready for vision-Centric Driving Streaming Perception? The ASAP Benchmark

Are We Ready for Vision-Centric Driving Streaming Perception...

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

作者： Wang, Xiaofeng Zhu, Zheng Zhang, Yunpeng Huang, Guan Ye, Yun Xu, Wenbo Chen, Ziwei Wang, Xingang Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China PhiGent Robot Beijing Peoples R China Southeast Univ Dhaka Bangladesh

ISBN: (纸本)9798350301298

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://***/JeffWang987/ASAP.

关键词： Autonomous driving

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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...

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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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A Soma Segmentation Benchmark in Full Adult Fly Brain

A Soma Segmentation Benchmark in Full Adult Fly Brain

引用

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

作者： Liu, Xiaoyu Hu, Bo Li, Mingxing Huang, Wei Zhang, Yueyi Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Neuron reconstruction in a full adult fly brain from high-resolution electron microscopy (EM) data is regarded as a cornerstone for neuroscientists to explore how neurons inspire intelligence. As the central part of neurons, somas in the full brain indicate the origin of neurogenesis and neural functions. However, due to the absence of EM datasets specifically annotated for somas, existing deep learning-based neuron reconstruction methods cannot directly provide accurate soma distribution and morphology. Moreover, full brain neuron reconstruction remains extremely time-consuming due to the unprecedentedly large size of EM data. In this paper, we develop an efficient soma reconstruction method for obtaining accurate soma distribution and morphology information in a full adult fly brain. To this end, we first make a high-resolution EM dataset with fine-grained 3D manual annotations on somas. Relying on this dataset, we propose an efficient, two-stage deep learning algorithm for predicting accurate locations and boundaries of 3D soma instances. Further, we deploy a parallelized, high-throughput data processing pipeline for executing the above algorithm on the full brain. Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective. We release our code and dataset at https://***/liuxy1103/EMADS.

关键词： cell microscopy Medical and biological vision

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Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Language in a Bottle: Language Model Guided Concept Bottlene...

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

作者： Yang, Yue Panagopoulou, Artemis Zhou, Shenghao Jin, Daniel Callison-Burch, Chris Yatskar, Mark Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9798350301298

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.(1)

关键词： Explainable computer vision

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Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data

Best of Both Worlds: Multimodal Contrastive Learning with Ta...

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

作者： Hager, Paul Menten, Martin J. Rueckert, Daniel Tech Univ Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Imperial Coll London London England

ISBN: (纸本)9798350301298

Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes and expensive annotation costs, the necessity for unsupervised methods that can pretrain multimodally and predict unimodally has risen. To address these needs, we propose the first self-supervised contrastive learning framework that takes advantage of images and tabular data to train unimodal encoders. Our solution combines SimCLR and SCARF, two leading contrastive learning strategies, and is simple and effective. In our experiments, we demonstrate the strength of our framework by predicting risks of myocardial infarction and coronary artery disease (CAD) using cardiac MR images and 120 clinical features from 40,000 UK Biobank subjects. Furthermore, we show the generalizability of our approach to natural images using the DVM car advertisement dataset. We take advantage of the high interpretability of tabular data and through attribution and ablation experiments find that morphometric tabular features, describing size and shape, have outsized importance during the contrastive learning process and improve the quality of the learned embeddings. Finally, we introduce a novel form of supervised contrastive learning, label as a feature (LaaF), by appending the ground truth label as a tabular feature during multimodal pretraining, outperforming all supervised contrastive baselines.

关键词： cell microscopy Medical and biological vision

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Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

Mapping Degeneration Meets Label Evolution: Learning Infrare...

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

作者： Ying, Xinyi Liu, Li Wang, Yingqian Li, Ruojing Chen, Nuo Lin, Zaiping Sheng, Weidong Zhou, Shilin Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt to achieve infrared small target detection with point-level supervision. Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels. Motivated by this "mapping degeneration" phenomenon, we propose a label evolution framework named label evolution with single point supervision (LESPS) to progressively expand the point label by leveraging the intermediate predictions of CNNs. In this way, the network predictions can finally approximate the updated pseudo labels, and a pixel-level target mask can be obtained to train CNNs in an end-to-end manner. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Experimental results show that CNNs equipped with LESPS can well recover the target masks from corresponding point labels, and can achieve over 70% and 95% of their fully supervised performance in terms of pixel-level intersection over union (IoU) and object-level probability of detection (Pd), respectively. Code is available at https://***/XinyiYing/LESPS.

关键词： detection recognition: Categorization retrieval

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