273 citations · 1.4k across the 86 of their papers we have counts for
90 papers
SoccerNet 2026 Challenges Results
Anthony Cioppa, Silvio Giancola, Håkan Ardö +102
The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…
Inferring Dynamic Physical Properties from Video Foundation Models
Guanqi Zhan, Xianzheng Ma, Weidi Xie +1
We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal information to be inferred: elasti…
Character-Centric Understanding of Animated Movies
Zhongrui Gui, Junyu Xie, Tengda Han +2
Animated movies are captivating for their unique character designs and imaginative storytelling, yet they pose significant challenges for existing recognition systems. Unlike the c…
Shot-by-Shot: Film-Grammar-Aware Training-Free Audio Description Generation
Junyu Xie, Tengda Han, Max Bain +5
Our objective is the automatic generation of Audio Descriptions (ADs) for edited video material, such as movies and TV series. To achieve this, we propose a two-stage framework tha…
ELIP: Enhanced Visual-Language Foundation Models for Image Retrieval
Guanqi Zhan, Yuanpei Liu, Kai Han +2
The objective in this paper is to improve the performance of text-to-image retrieval. To this end, we introduce a new framework that can boost the performance of large-scale pre-tr…
AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description
Junyu Xie, Tengda Han, Max Bain +4
Our objective is to generate Audio Descriptions (ADs) for both movies and TV series in a training-free manner. We use the power of off-the-shelf Visual-Language Models (VLMs) and L…
AutoAD III: The Prequel -- Back to the Pixels
Tengda Han, Max Bain, Arsha Nagrani +3
Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently, vi…
Made to Order: Discovering monotonic temporal changes via self-supervised video ordering
Charig Yang, Weidi Xie, Andrew Zisserman
Our objective is to discover and localize monotonic temporal changes in a sequence of images. To achieve this, we exploit a simple proxy task of ordering a shuffled image sequence,…
Moving Object Segmentation: All You Need Is SAM (and Flow)
Junyu Xie, Charig Yang, Weidi Xie +1
The objective of this paper is motion segmentation -- discovering and segmenting the moving objects in a video. This is a much studied area with numerous careful, and sometimes com…
Synchformer: Efficient Synchronization from Sparse Cues
Vladimir Iashin, Weidi Xie, Esa Rahtu +1
Our objective is audio-visual synchronization with a focus on 'in-the-wild' videos, such as those on YouTube, where synchronization cues can be sparse. Our contributions include a…
Amodal Ground Truth and Completion in the Wild
Guanqi Zhan, Chuanxia Zheng, Weidi Xie +1
This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work, the amodal segmen…
Appearance-Based Refinement for Object-Centric Motion Segmentation
Junyu Xie, Weidi Xie, Andrew Zisserman
The goal of this paper is to discover, segment, and track independently moving objects in complex visual scenes. Previous approaches have explored the use of optical flow for motio…
Self-supervised Object-Centric Learning for Videos
Görkay Aydemir, Weidi Xie, Fatma Güney
Unsupervised multi-object segmentation has shown impressive results on images by utilizing powerful semantics learned from self-supervised pretraining. An additional modality such…
AutoAD II: The Sequel -- Who, When, and What in Movie Audio Description
Tengda Han, Max Bain, Arsha Nagrani +3
Audio Description (AD) is the task of generating descriptions of visual content, at suitable time intervals, for the benefit of visually impaired audiences. For movies, this presen…
A General Protocol to Probe Large Vision Models for 3D Physical Understanding
Guanqi Zhan, Chuanxia Zheng, Weidi Xie +1
Our objective in this paper is to probe large vision models to determine to what extent they 'understand' different physical properties of the 3D scene depicted in an image. To thi…
The Making and Breaking of Camouflage
Hala Lamdouar, Weidi Xie, Andrew Zisserman
Not all camouflages are equally effective, as even a partially visible contour or a slight color difference can make the animal stand out and break its camouflage. In this paper, w…
Diagnosing Human-object Interaction Detectors
Fangrui Zhu, Yiming Xie, Weidi Xie +1
We have witnessed significant progress in human-object interaction (HOI) detection. The reliance on mAP (mean Average Precision) scores as a summary metric, however, does not provi…
Joint-Relation Transformer for Multi-Person Motion Prediction
Qingyao Xu, Weibo Mao, Jingze Gong +5
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based meth…
Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data
Chaoyi Wu, Xiaoman Zhang, Ya Zhang +2
In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM. We consider the construction of foundational models from three perspectives, namel…
arXiVeri: Automatic table verification with GPT
Gyungin Shin, Weidi Xie, Samuel Albanie
Without accurate transcription of numerical data in scientific documents, a scientist cannot draw accurate conclusions. Unfortunately, the process of copying numerical data from on…
Multi-Modal Classifiers for Open-Vocabulary Object Detection
Prannay Kaul, Weidi Xie, Andrew Zisserman
The goal of this paper is open-vocabulary object detection (OVOD) $\unicode{x2013}$ building a model that can detect objects beyond the set of categories seen at training, thus ena…
Boost Video Frame Interpolation via Motion Adaptation
Haoning Wu, Xiaoyun Zhang, Weidi Xie +2
Video frame interpolation (VFI) is a challenging task that aims to generate intermediate frames between two consecutive frames in a video. Existing learning-based VFI methods have…
Zero-shot Composed Text-Image Retrieval
Yikun Liu, Jiangchao Yao, Ya Zhang +2
In this paper, we consider the problem of composed image retrieval (CIR), it aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retri…
Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
Chang Liu, Haoning Wu, Yujie Zhong +3
Generative models have recently exhibited exceptional capabilities in text-to-image generation, but still struggle to generate image sequences coherently. In this work, we focus on…