Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.
ISTI - CNR（意大利国家研究委员会信息科学与技术研究所）；University of Geneva（日内瓦大学）
Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a novel approach called Transformer Encoder Reasoning and Alignment Network (TERAN). TERAN enforces a fine-grained match between the underlying components of images and sentences, i.e., image regions and words, respectively, in order to preserve the inform…
Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems. In this paper, we focus on leveraging multi-modal content in the form of visual and textual cues to tackle the task of fine-grained image classification and retrieval. First, we obtain the text instances from images by employing a text reading system. Then, we combine textual features with salient image regions to exploit the complementary information carried by the two sources. Specifically, we employ a Graph Convolutional Network …
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；National Engineering Research Center of Robot Visual Perception and Control Technology（机器人视觉感知与控制技术国家工程研究中心）；College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）
We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Sta…
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…
Fine-grained classification remains a very challenging problem, because of the absence of well-labeled training data caused by the high cost of annotating a large number of fine-grained categories. In the extreme case, given a set of test categories without any well-labeled training data, the majority of existing works can be grouped into the following two research directions: 1) crawl noisy labeled web data for the test categories as training data, which is dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary categories with well-labeled training data to the test cate…
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 …
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所基础模型研究中心）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）；Objecteye Inc., Beijing, China（Objecteye公司）；Central South University, Hunan, China（中南大学）
Tencent YouTu Lab（腾讯优图实验室）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Text-based Person Search (TPS), is targeted on retrieving pedestrians to match text descriptions instead of query images. Recent Vision-Language Pre-training (VLP) models can bring transferable knowledge to downstream TPS tasks, resulting in more efficient performance gains. However, existing TPS methods improved by VLP only utilize pre-trained visual encoders, neglecting the corresponding textual representation and breaking the significant modality alignment learned from large-scale pre-training. In this paper, we explore the full utilization of textual potential from VLP in TPS tasks. We bui…
Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model
Dalian Maritime University（大连海事大学）；Zhejiang Sci-Tech University（浙江理工大学）；Beijing University of Technology（北京工业大学）；Dalian University of Technology（大连理工大学）
FB-CLIP: Fine-Grained Zero-Shot Anomaly Detection with Foreground-Background Disentanglement
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（西安光学精密机械研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
CADFormer: Fine-Grained Cross-modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）
Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-language models (VLMs) to recognize potentially anomalous regions through feature similarity between text descriptions and images. However, due to the lack of detailed textual descriptions, these methods can only pre-define image-level descriptions to match each visual patch token to identify potential anomalous regions, which leads to the semantic misalignment between image descriptions and patch-level visual anomali…
Hong Kong University of Science and Technology（香港科技大学）；Sun Yat-sen University（中山大学）
Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a…
Powerful generative adversarial networks (GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating simple images such as flowers from captions. In this work, we extend this problem to the less addressed domain of face generation from fine-grained textual descriptions of face, e.g., "A person has curly hair, oval face, and mustache". We are motivated by the potential of automated face generation to impact and assist critical tasks such as criminal face reconstruction. Since current datasets for the task are either ve…
FG-MDM: Towards Zero-Shot Human Motion Generation via ChatGPT-Refined Descriptions
Nanjing University of Science and Technology（南京理工大学）；Shandong University（山东大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Normal University（北京师范大学）
Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；Watrix Technology Limited Co. Ltd.（华瑞芯科技有限公司）
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy & Zhongguancun Institute of Artificial Intelligence, Beijing, China（北京中关村学院及中关村人工智能研究院）；Department of Molecular Imaging, Royal Prince Alfred Hospital（皇家珀斯阿尔弗雷德医院分子成像部）
FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing
Shenzhen University（深圳大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；University of Nottingham（诺丁汉大学）
Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；Hupan Laboratory（湖畔实验室）；Fuzhou University（福州大学）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Chang Gung Memorial Hospital（长庚纪念医院）；Zhongshan Hospital(Xiamen), Fudan University（复旦大学附属中山医院厦门医院）
Renmin University of China（中国人民大学）；University of Adelaide（阿德莱德大学）
Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach for this problem is to learn a joint embedding space to measure cross-modal similarities. However, simple joint embeddings are insufficient to represent complicated visual and textual details, such as scenes, objects, actions and their compositions. To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels. To be specific, the mode…
Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval
Shanghai Jiao Tong University（上海交通大学）；Institute of Digital Twin, Eastern Institute of Technology（数字孪生研究院，东部技术研究所）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）
Tsinghua University（清华大学）；Meituan Inc（美团公司）；Central South University（中南大学）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanyang Technological University（南洋理工大学）
Towards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
DUT-RU International School of Information Science & Engineering, Dalian University of Technology, China（大连理工大学国际信息科学与工程学院，大连理工大学，中国）；Beihang University, China（北京航空航天大学，中国）；The University of Sydney, Australia（悉尼大学，澳大利亚）
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）；Objecteye Inc.（Objecteye公司）
