Improving accuracy and speeding up Document Image Classification through parallel systems
Barcelona Supercomputing Center - Centro Nacional de Supercomputación（巴塞罗那超级计算中心-国家超级计算中心）；Universitat Politècnica de Catalunya, UPC-BarcelonaTech（加泰罗尼亚理工大学（UPC-巴塞罗那理工））；Serimag Media - TAAD；CaixaBank（凯克萨银行）
Javier Ferrando, Juan Luis Dominguez, Jordi Torres, Raul Garcia, David Garcia, Daniel Garrido, Jordi Cortada, Mateo Valero
This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482. Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks.
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
Department of Computer Science, University of Freiburg, Germany（弗赖堡大学计算机科学系）；Artificial Intelligence Research Center, AIST, Tokyo, Japan（日本科学技术厅人工智能研究中心）
Cinnamon AI（肉桂人工智能公司）；Hung Yen University of Technology and Education（兴安技术教育大学）；Deakin University（迪肯大学）
Successful Artificial Intelligence systems often require numerous labeled data to extract information from document images. In this paper, we investigate the problem of improving the performance of Artificial Intelligence systems in understanding document images, especially in cases where training data is limited. We address the problem by proposing a novel finetuning method using reinforcement learning. Our approach treats the Information Extraction model as a policy network and uses policy gradient training to update the model to maximize combined reward functions that complement the traditi…
Claremont Graduate University（克莱蒙特研究生大学）；Naval Air Warfare Center（海军空战中心）；Claremont McKenna College（克莱蒙特·麦肯纳学院）
We propose a method to improve image clustering using sparse text and the wisdom of the crowds. In particular, we present a method to fuse two different kinds of document features, image and text features, and use a common dictionary or "wisdom of the crowds" as the connection between the two different kinds of documents. With the proposed fusion matrix, we use topic modeling via non-negative matrix factorization to cluster documents.
Jadavpur University（贾达布尔大学）；Nanyang Technological University（南洋理工大学）；Cognizant Technology Solutions（高知特科技解决方案公司）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
Binarization of degraded document images is an elementary step in most of the problems in document image analysis domain. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. In this way, the network creates multiple versions of the same textual content with various noisy textures, thus enlarging the available document binarization datasets. At last, the newly generated images are passed through a Binarization ne…
Docopilot: Improving Multimodal Models for Document-Level Understanding
Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Nanjing University（南京大学）；Nankai University（南开大学）；Fudan University（复旦大学）；Tsinghua University（清华大学）；SenseTime Research（商汤科技研究院）
Université Paris-Saclay, ENS Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France（巴黎-萨克雷大学、巴黎-萨克雷高等师范学院、Borelli中心，法国）；IIE, Facultad de Ingenería, Universidad de la República（国际经济学院、工程学院，乌拉圭）；City University of Hong Kong, Kowloon, Hong Kong（香港城市大学，九龙，香港）；Determinant France, Paris, France（法国Determinant公司，巴黎，法国）；Sadjad University, Mashhad, Iran（沙贾尔大学，马什哈德，伊朗）
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongke Fanyu Technology Co., Ltd（中科泛语科技有限公司）
Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visua…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate for vision-and-language summarization. We propose CLIPBERTScore, a simple weighted combination of CLIPScore and BERTScore to leverage the robustness and strong factuality detection performance between image-summary and document-summary, respectively. Next, due to the lack of meta-evaluation benchmarks to evaluate the quality of multimodal factuality metrics, we collect human judgments of f…
