In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video/sentences from 92 DVDs and is growing.
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation
Sichuan University（四川大学）；Case Western Reserve University（凯斯西储大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Urgench State University（乌尔根奇国立大学）；Gansu Agricultural University（甘肃农业大学）
Body language is an eye-catching social signal and its automatic analysis can significantly advance artificial intelligence systems to understand and actively participate in social interactions. While computer vision has made impressive progress in low-level tasks like head and body pose estimation, the detection of more subtle behaviors such as gesturing, grooming, or fumbling is not well explored. In this paper we present BBSI, the first set of annotations of complex Bodily Behaviors embedded in continuous Social Interactions in a group setting. Based on previous work in psychology, we manua…
Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations
Tsinghua University（清华大学）；ByteDance（字节跳动）；Digital Center, China Merchants Group Limited（招商银行集团数字中心）；Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）
Computer Vision Centre, Universitat Autonoma de Barcelona（巴塞罗那自治大学计算机视觉中心）
The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become a de-facto evaluation standard for robust reading systems and algorithms. Concurrent with its second incarnation in 2011, a continuous effort started to develop an on-line framework to facilitate the hosting and management of competitions. This paper outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the competitions. The RRC Annotation and Evaluation Platform is a modular framework, fully accessible through on-line interfaces. It comprises a collection …
In recent years, supervised learning has become the dominant paradigm for training deep-learning based methods for 3D object detection. Lately, the academic community has studied 3D object detection in the context of autonomous vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse 2.0. However, these datasets may have incomplete annotations, often only labeling a small subset of objects in a scene. Although commercial services exists for 3D bounding box annotation, these are often prohibitively expensive. To address these limitations, we propose ReBound, an open-sourc…
Binghamton University, SUNY（纽约州立大学宾汉姆顿分校）；The U.S. Air Force Research Laboratory（美国空军研究实验室）
Situation AWareness (SAW) is essential for many mission critical applications. However, SAW is very challenging when trying to immediately identify objects of interest or zoom in on suspicious activities from thousands of video frames. This work aims at developing a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios like public safety monitoring, the features of objects of interest may be much more complicated than face features. In addition, human operators may not be always able to provide a descriptive, simple, and accura…
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Nvidia（英伟达）
Inferring detailed 3D geometry of the scene is crucial for robotics applications, simulation, and 3D content creation. However, such information is hard to obtain, and thus very few datasets support it. In this paper, we propose an interactive framework for annotating 3D object geometry from both point cloud data and RGB imagery. The key idea behind our approach is to exploit strong priors that humans have about the 3D world in order to interactively annotate complete 3D shapes. Our framework targets naive users without artistic or graphics expertise. We introduce two simple-to-use interaction…
University of Southern California（南加州大学）；Microsoft Research（微软研究院）
Today, ground-truth generation uses data sets annotated by cloud-based annotation services. These services rely on human annotation, which can be prohibitively expensive. In this paper, we consider the problem of hybrid human-machine labeling, which trains a classifier to accurately auto-label part of the data set. However, training the classifier can be expensive too. We propose an iterative approach that minimizes total overall cost by, at each step, jointly determining which samples to label using humans and which to label using the trained classifier. We validate our approach on well known…
IBM China Research Lab（IBM中国研究院）；Shanghai Jiao Tong University（上海交通大学）
With the rapid growth of display devices, quality inspection via machine vision technology has become increasingly important for flat-panel displays (FPD) industry. This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. The system is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture. A hybrid application of conventional computer vision technique and the latest deep convolutional neural network (DCNN) leads to an integrated defec…
Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research（瓦赫宁根大学与研究中心地理信息科学与遥感实验室）
Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automat…
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（瓦伦西亚理工大学以人为中心技术大学研究院）；valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence（瓦伦西亚人工智能研究生院及研究网络）；ÉTS Montréal（蒙特利尔工程学院）
