22 citations · 30 across the 16 of their papers we have counts for
16 papers
Character-aware audio-visual subtitling in context
Jaesung Huh, Andrew Zisserman
This paper presents an improved framework for character-aware audio-visual subtitling in TV shows. Our approach integrates speech recognition, speaker diarisation, and character re…
The VoxCeleb Speaker Recognition Challenge: A Retrospective
Jaesung Huh, Joon Son Chung, Arsha Nagrani +4
The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of…
3D-Aware Instance Segmentation and Tracking in Egocentric Videos
Yash Bhalgat, Vadim Tschernezki, Iro Laina +3
Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces…
OVR: A Dataset for Open Vocabulary Temporal Repetition Counting in Videos
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +1
We introduce a dataset of annotations of temporal repetitions in videos. The dataset, OVR (pronounced as over), contains annotations for over 72K videos, with each annotation speci…
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
Mark Hamilton, Andrew Zisserman, John R. Hershey +1
We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visually aligned features solely through watching vi…
A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision
Charles Raude, K R Prajwal, Liliane Momeni +4
In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer…
TIM: A Time Interval Machine for Audio-Visual Action Recognition
Jacob Chalk, Jaesung Huh, Evangelos Kazakos +2
Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events…
FlexCap: Describe Anything in Images in Controllable Detail
Debidatta Dwibedi, Vidhi Jain, Jonathan Tompson +2
We introduce FlexCap, a vision-language model that generates region-specific descriptions of varying lengths. FlexCap is trained to produce length-conditioned captions for input bo…
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields
Yash Bhalgat, Iro Laina, João F. Henriques +2
Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2),…
A SOUND APPROACH: Using Large Language Models to generate audio descriptions for egocentric text-audio retrieval
Andreea-Maria Oncescu, João F. Henriques, Andrew Zisserman +2
Video databases from the internet are a valuable source of text-audio retrieval datasets. However, given that sound and vision streams represent different "views" of the data, trea…
Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling
Bruno Korbar, Jaesung Huh, Andrew Zisserman
The goal of this paper is automatic character-aware subtitle generation. Given a video and a minimal amount of metadata, we propose an audio-visual method that generates a full tra…
Text-Conditioned Resampler For Long Form Video Understanding
Bruno Korbar, Yongqin Xian, Alessio Tonioni +2
In this paper we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequ…
A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames
Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak +7
Understanding long, real-world videos requires modeling of long-range visual dependencies. To this end, we explore video-first architectures, building on the common paradigm of tra…
Learning from One Continuous Video Stream
João Carreira, Michael King, Viorica Pătrăucean +9
We introduce a framework for online learning from a single continuous video stream -- the way people and animals learn, without mini-batches, data augmentation or shuffling. This p…
No Representation Rules Them All in Category Discovery
Sagar Vaze, Andrea Vedaldi, Andrew Zisserman
In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically, given a dataset with labelled and unlabelled images, the task is to cluster all images in…
Show from Tell: Audio-Visual Modelling in Clinical Settings
Jianbo Jiao, Mohammad Alsharid, Lior Drukker +3
Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modell…