Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies
Wenzhou University Artificial Intelligence and Advanced Manufacturing Institute (AIAMI)（温州大学人工智能与先进制造研究院）；Graduate School of Engineering, Tohoku University（东京东京大学工学研究科）
Most existing text recognition methods are trained on large-scale synthetic datasets due to the scarcity of labeled real-world datasets. Synthetic images, however, cannot faithfully reproduce real-world scenarios, such as uneven illumination, irregular layout, occlusion, and degradation, resulting in performance disparities when handling complex real-world images. Recent self-supervised learning techniques, notably contrastive learning and masked image modeling (MIM), narrow this domain gap by exploiting unlabeled real text images. This study first analyzes the original Masked AutoEncoder (MAE) and observes that random patch masking predominantly captures low-level textural features but misses high-level contextual representations. To fully exploit the high-level contextual representations, we introduce random blockwise and span masking in the text recognition task. These strategies can mask the continuous image patches and completely remove some characters, forcing the model to infer relationships among characters within a word. Our Multi-Masking Strategy (MMS) integrates random patch, blockwise, and span masking into the MIM frame, which jointly learns low and high-level textual representations. After fine-tuning with real data, MMS outperforms the state-of-the-art self-supervised methods in various text-related tasks, including text recognition, segmentation, and text-image super-resolution.
Optical Character Recognition (OCR), the task of extracting textual information from scanned documents is a vital and broadly used technology for digitizing and indexing physical documents. Existing technologies perform well for clean documents, but when the document is visually degraded, or when there are non-textual elements, OCR quality can be greatly impacted, specifically due to erroneous detections. In this paper we present an improved detection network with a masking system to improve the quality of OCR performed on documents. By filtering non-textual elements from the image we can util…
Communication University of China（中国传媒大学）；Tsinghua University（清华大学）；AI Research（360人工智能研究院）
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Distillation (DMJD). For disjoint masking (DM), we sequentially sample multiple masked views per image in a mini-batch with the disjoint regulation to raise the usage of tokens for reconstruction in each image while keeping the masking rate of …
The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming. In this paper, we propose to exploit shape information via masking…
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
Visual context provides grounding information for multimodal machine translation (MMT). However, previous MMT models and probing studies on visual features suggest that visual information is less explored in MMT as it is often redundant to textual information. In this paper, we propose an object-level visual context modeling framework (OVC) to efficiently capture and explore visual information for multimodal machine translation. With detected objects, the proposed OVC encourages MMT to ground translation on desirable visual objects by masking irrelevant objects in the visual modality. We equip…
In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual…
In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking d…
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）
Fine-grained visual categorization is to recognize hundreds of subcategories belonging to the same basic-level category, which is a highly challenging task due to the quite subtle and local visual distinctions among similar subcategories. Most existing methods generally learn part detectors to discover discriminative regions for better categorization performance. However, not all parts are beneficial and indispensable for visual categorization, and the setting of part detector number heavily relies on prior knowledge as well as experimental validation. As is known to all, when we describe the …
We introduce a multimodal visual-textual search refinement method for fashion garments. Existing search engines do not enable intuitive, interactive, refinement of retrieved results based on the properties of a particular product. We propose a method to retrieve similar items, based on a query item image and textual refinement properties. We believe this method can be leveraged to solve many real-life customer scenarios, in which a similar item in a different color, pattern, length or style is desired. We employ a joint embedding training scheme in which product images and their catalog textua…
NavInfo Europe（四维图新欧洲公司）；Eindhoven University of Technology（埃因霍温理工大学）
Self-supervised monocular depth estimation is a salient task for 3D scene understanding. Learned jointly with monocular ego-motion estimation, several methods have been proposed to predict accurate pixel-wise depth without using labeled data. Nevertheless, these methods focus on improving performance under ideal conditions without natural or digital corruptions. The general absence of occlusions is assumed even for object-specific depth estimation. These methods are also vulnerable to adversarial attacks, which is a pertinent concern for their reliable deployment in robots and autonomous drivi…
Exploring Motion-Language Alignment for Text-driven Motion Generation
Department of Automation, University of Science and Technology of China（中国科学技术大学自动化系）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，北京通用人工智能研究院）
Google Research（谷歌研究院）；University of California, Los Angeles（加州大学洛杉矶分校）
In this paper, we explore the possibility of building a unified foundation model that can be adapted to both vision-only and text-only tasks. Starting from BERT and ViT, we design a unified transformer consisting of modality-specific tokenizers, a shared transformer encoder, and task-specific output heads. To efficiently pre-train the proposed model jointly on unpaired images and text, we propose two novel techniques: (i) We employ the separately-trained BERT and ViT models as teachers and apply knowledge distillation to provide additional, accurate supervision signals for the joint training; …
Nanyang Technological University（南洋理工大学）；ByteDance Inc.（字节跳动公司）；University of Oxford（牛津大学）
Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text images. However, these methods cannot well cope with OCR tasks because of the difficulty in both instance-level text encoding and image-text pair acquisition (i.e. images and captured texts in them). This paper presents a weakly supervised pre-training method, oCLIP, which can acquire effective scene tex…
Joint raveled or spalled damage (henceforth called joint damage) can affect the safety and long-term performance of concrete pavements. It is important to assess and quantify the joint damage over time to assist in building action plans for maintenance, predicting maintenance costs, and maximize the concrete pavement service life. A framework for the accurate, autonomous, and rapid quantification of joint damage with a low-cost camera is proposed using a computer vision technique with a deep learning (DL) algorithm. The DL model is employed to train 263 images of sawcuts with joint damage. The…
Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Sir Run Run Shaw Hospital（邵逸夫医院）；Lenovo Research（联想研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang Normal University（浙江师范大学）
University of Glasgow（格拉斯哥大学）；TAL Education Group（好未来教育集团）；National University of Singapore（新加坡国立大学）
The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments, like regions in images and words in sentences, and adopt attention modules to highlight the relevance of cross-modal semantic correspondences. However, the retrieval performance remains unsatisfactory due to a lack of consistent representation in both semantics and structural spaces. In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e.g., "dog $\to$ play $\to$ ba…
Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model
Information & Computer Science Department, King Fahd University of Petroleum & Minerals（国王法赫德石油与矿物大学信息与计算机科学系）；Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）
In this paper we tackle the cross-modal video retrieval problem and, more specifically, we focus on text-to-video retrieval. We investigate how to optimally combine multiple diverse textual and visual features into feature pairs that lead to generating multiple joint feature spaces, which encode text-video pairs into comparable representations. To learn these representations our proposed network architecture is trained by following a multiple space learning procedure. Moreover, at the retrieval stage, we introduce additional softmax operations for revising the inferred query-video similarities…
Microsoft Dynamics 365 AI Research（微软 Dynamics 365 AI 研究院）
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image…
Nanjing University of information science and Technology（南京信息工程大学）；Xinjiang University（新疆大学）；Nanjing Institute of Technology（南京工程学院）；KingSaud University（沙特国王大学）
Text-based person re-identification(Re-id) is an important task in video surveillance, which consists of retrieving the corresponding person's image given a textual description from a large gallery of images. It is difficult to directly match visual contents with the textual descriptions due to the modality heterogeneity. On the one hand, the textual embeddings are not discriminative enough, which originates from the high abstraction of the textual descriptions. One the other hand,Global average pooling (GAP) is commonly utilized to extract more general or smoothed features implicitly but igno…
Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression. One major difficulty to train such language-based image segmentation systems is the lack of datasets with joint vision and text annotations. Although existing vision datasets such as MS COCO provide image captions, there are few datasets with region-level textual annotations for images, and these are often small…
Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction
School of Information Engineering, Nanchang University, Nanchang, China（信息工程学院，南昌大学，南昌，中国）；School of Mathematics and Computer Sciences, Nanchang University, Nanchang, China（数学与计算机科学学院，南昌大学，南昌，中国）
Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. However, most existing methods can only learn from aligned image-caption data and rely heavily on expensive regional features, which greatly limits their scalability and performance. In this paper, we propose an end-to-end unified-modal pre-training framework, namely UNIMO-2, for joint learning on both aligned image-caption data and unaligned image-only and text-only corpus. We build a unified Transformer model to jointly learn visual representations, textual representations and sema…
Two-stage Vision Transformers and Hard Masking offer Robust Object Representations
Inria, EVERGREEN, University of Montpellier（法国国家信息与自动化研究所，EVERGREEN团队，蒙彼利埃大学）；INRAE, UMR TETIS, University of Montpellier（法国国家农业、食品与环境研究所，TETIS联合研究单位，蒙彼利埃大学）
Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究院）；Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术学院）
Georgia Institute of Technology（佐治亚理工学院）；Wuhan University of Technology（武汉理工大学）
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. The first tier improves recipe text embedding by optimizing the LSTM networks with term extraction and ranking enhanced sequence patterns, and optimizes the image embedding by combining the ResNeXt-101 image encoder with the category embedding using wideResNet-50 with word2vec. The second tier modality alignment optimizes the textual-visual joint embedding loss function using a double batch-hard triplet loss with soft…
Telefonica Research（西班牙电信研究中心）；Universitat Politecnica de Catalunya (UPC)（加泰罗尼亚理工大学（UPC））
In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows f…
École des Ponts（巴黎桥路学院）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；CNRS（法国国家科学研究中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
We address the problem of generating diverse 3D human motions from textual descriptions. This challenging task requires joint modeling of both modalities: understanding and extracting useful human-centric information from the text, and then generating plausible and realistic sequences of human poses. In contrast to most previous work which focuses on generating a single, deterministic, motion from a textual description, we design a variational approach that can produce multiple diverse human motions. We propose TEMOS, a text-conditioned generative model leveraging variational autoencoder (VAE)…
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems
Ho Chi Minh City University of Technology (HCMUT)（胡志明市理工大学）；Vietnam National University Ho Chi Minh City（越南国家大学胡志明市分校）；National University of Singapore（新加坡国立大学）
Korea Advanced Institute of Science and Technology (KAIST)（韩国高等科学技术研究院（KAIST））；Lunit Inc.（Lunit公司）
Visual-semantic embedding enables various tasks such as image-text retrieval, image captioning, and visual question answering. The key to successful visual-semantic embedding is to express visual and textual data properly by accounting for their intricate relationship. While previous studies have achieved much advance by encoding the visual and textual data into a joint space where similar concepts are closely located, they often represent data by a single vector ignoring the presence of multiple important components in an image or text. Thus, in addition to the joint embedding space, we propo…
Aalborg University（奥尔堡大学）；University Politehnica of Bucharest（布加勒斯特理工大学）；University of Bucharest（布加勒斯特大学）；Milestone Systems（迈尔斯通系统公司）；SecurifAI（SecurifAI公司）
本文提出 CL-MAE，利用可学习掩码模块在 MAE 自监督训练中生成由易到难的重建课程，并在 ImageNet 及五个下游任务上验证其优于 MAE 的表示学习效果。
