Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting transparent objects is challenging because these objects have diverse appearance inherited from the image background, making them had similar appearance with their surroundings. Besides the technical difficulty of this task, only a few previous datasets were specially designed and collected to explore this task and most of the existing datasets have major drawbacks. They either possess limited sample size such as merely a thousand of images without manual annotations, or they generate all images by using computer graphics method (i.e. not real image). To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10,428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets. The transparent objects in Trans10K are extremely challenging due to high diversity in scale, viewpoint and occlusion as shown in Fig. 1. To evaluate the effectiveness of Trans10K, we propose a novel boundary-aware segmentation method, termed TransLab, which exploits boundary as the clue to improve segmentation of transparent objects. Extensive experiments and ablation studies demonstrate the effectiveness of Trans10K and validate the practicality of learning object boundary in TransLab. For example, TransLab significantly outperforms 20 recent object segmentation methods based on deep learning, showing that this task is largely unsolved. We believe that both Trans10K and TransLab have important contributions to both the academia and industry, facilitating future researches and applications.
The University of Hong Kong（香港大学）；Sensetime Research（商汤科技研究院）；Nanjing University（南京大学）
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application. (2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version. Furthermore, a novel transforme…
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心Eurecat）
Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the originally released semantic labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset. Additionally, two object-based data augmentation techniques are deployed to synthetically enrich under-represented categories which, in benchmarking …
The University of Tokyo（东京大学）；Carnegie Mellon University（卡内基梅隆大学）
We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e.g., outdoors) when we only have labeled images taken under very different conditions (e.g., indoors). In the real world, it is important that the model trained for both tasks works under various imaging conditions. However, their variation covered by existing labeled hand datasets is limited. Thus, it is necessary to adapt the model trained on the labeled images (source) to unlabeled images (target) with unseen imaging conditions. While self-training domain adapt…
The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D ves…
University of Warwick（华威大学）；NRP Co.（NRP公司）；Royal Wolverhampton NHS Trust（皇家伍尔弗汉普顿NHS信托）；University of Sheffield（谢菲尔德大学）；University Hospitals Coventry and Warwickshire（考文垂和沃里克郡大学医院）
The emerging area of computational pathology (CPath) is ripe ground for the application of deep learning (DL) methods to healthcare due to the sheer volume of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides. However, it is imperative for the DL algorithms relying on nuclei-level details to be able to cope with data from `the clinical wild', which tends to be quite challenging. We study, and extend recently released PanNuke dataset consisting of ~200,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei i…
University of Dayton（代顿大学）；Institute of Industrial Science, the University of Tokyo（东京大学生产技术研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学）；University of Science, VNU-HCM（越南国立大学胡志明市分校科学大学）；National Institute of Informatics（国立信息学研究所）
Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spite of a wide range of potential applications including the preservation of wild animals and the discovery of new species, surveillance systems, search-and-rescue missions in the event of natural disasters such as earthquakes, floods or hurricanes. Thi…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Adobe Research（Adobe 研究院）
We consider the problem of segmenting image regions given a natural language phrase, and study it on a novel dataset of 77,262 images and 345,486 phrase-region pairs. Our dataset is collected on top of the Visual Genome dataset and uses the existing annotations to generate a challenging set of referring phrases for which the corresponding regions are manually annotated. Phrases in our dataset correspond to multiple regions and describe a large number of object and stuff categories as well as their attributes such as color, shape, parts, and relationships with other entities in the image. Our e…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sources, especially when the same object class may produce varied sounds from distinct interactions. To address this challenging problem, we propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and seg…
Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted. However, segmenting these safety-critical objects is rarely covered by conventional assistive technologies. To tackle this issue, we construct a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) model, which is capable of segmenting general and transparent objects and performing real-time wayfinding to assis…
Seg-Wild: Interactive Segmentation based on 3D Gaussian Splatting for Unconstrained Image Collections
College of Computer Science and Engineering, Shandong University of Science and Technology（山东科技大学计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）
提出基于3D Gaussian Splatting的交互式分割方法Seg-Wild，通过多维特征嵌入和Spiky 3D Gaussian Cutter处理无约束图像中的瞬态遮挡，实现更优的野外场景分割与重建质量。
Monocular Depth Estimation and Segmentation for Transparent Object with Iterative Semantic and Geometric Fusion
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation of Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Science and Technology, Shijiazhuang Tiedao University（石家庄铁道大学信息科学与技术学院）；D-Robotics（杭州宇树科技有限公司）
Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences (UCAS)（中国科学院大学先进交叉科学学院）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；Ant Group（蚂蚁集团）；Sangfor Technologies Inc.（深信服科技股份有限公司）
FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics
College of Computing, City University of Hong Kong（城市大学 computing 学院）；School of Data Science and Artificial Intelligence, Chang’an University（长安大学数据科学与人工智能学院）；School of Computer Science and Engineering, Ministry of Education Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-Sen University（中山大学计算机科学与工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学 computing 与数据科学学院）；Artificial Intelligence Lab. (Recod.ai) at the University of Campinas（坎皮纳斯大学人工智能实验室）
Google Research（谷歌研究院）；University of Oxford（牛津大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Computer vision is increasingly effective at segmenting objects in images and videos; however, scene effects related to the objects -- shadows, reflections, generated smoke, etc -- are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in vid…
Center for Visual Computing（视觉计算中心）；Centrale-Supelec（中央理工-高等电力学院）；Google Inc.（谷歌公司）；Visual Geometry Group（视觉几何组）；University of Oxford（牛津大学）
In this work we address the task of segmenting an object into its parts, or semantic part segmentation. We start by adapting a state-of-the-art semantic segmentation system to this task, and show that a combination of a fully-convolutional Deep CNN system coupled with Dense CRF labelling provides excellent results for a broad range of object categories. Still, this approach remains agnostic to high-level constraints between object parts. We introduce such prior information by means of the Restricted Boltzmann Machine, adapted to our task and train our model in an discriminative fashion, as a h…
Smoke segmentation is essential to precisely localize wildfire so that it can be extinguished in an early phase. Although deep neural networks have achieved promising results on image segmentation tasks, they are prone to be overconfident for smoke segmentation due to its non-rigid shape and transparent appearance. This is caused by both knowledge level uncertainty due to limited training data for accurate smoke segmentation and labeling level uncertainty representing the difficulty in labeling ground-truth. To effectively model the two types of uncertainty, we introduce a Bayesian generative …
Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion
The Hong Kong University of Science and Technology（香港科学与技术大学）；CFAR & IHPC, A*STAR（CFAR与IHPC，A*STAR）；Deakin University（德克萨斯大学）；Nankai University（南开大学）；National University of Singapore（新加坡国立大学）；Trinity College Dublin（都柏林大学）
German Cancer Research Center（德国癌症研究中心）；University of Heidelberg（海德堡大学）；Heidelberg University Hospital（海德堡大学医院）；caresyntax；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；SimulaMet；Arctic University of Norway（挪威北极大学）；Oslo Metropolitan University（奥斯都会大学）；University of Electronic Science and Technology of China（电子科技大学）；National Center for Tumor Diseases（国家肿瘤疾病中心）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden - Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；Universidad de los Andes（安第斯大学）；Peking University（北京大学）；Xiamen University（厦门大学）
Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should ge…
