49 citations · 159 across the 14 of their papers we have counts for
16 papers
BossouChimpanzee: Long-term Chimpanzee Video Dataset
Daniel Schofield, Susana Carvalho, Vladimir Iashin +10
We describe the BossouChimpanzee video dataset, a unique long-term visual record of wild chimpanzees at an outdoor laboratory for field experiments in Bossou, Guinea, spanning thre…
Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding
Arsha Nagrani, Jasper Uijilings, Shyamal Buch +6
Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation of the output (e.g. the ans…
More than a Moment: Towards Coherent Sequences of Audio Descriptions
Eshika Khandelwal, Junyu Xie, Tengda Han +5
Audio Descriptions (ADs) convey essential on-screen information, allowing visually impaired audiences to follow videos. To be effective, ADs must form a coherent sequence that help…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
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…
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…
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…
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…
LanSER: Language-Model Supported Speech Emotion Recognition
Taesik Gong, Josh Belanich, Krishna Somandepalli +3
Speech emotion recognition (SER) models typically rely on costly human-labeled data for training, making scaling methods to large speech datasets and nuanced emotion taxonomies dif…
Verbs in Action: Improving verb understanding in video-language models
Liliane Momeni, Mathilde Caron, Arsha Nagrani +2
Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language mo…
AutoAD: Movie Description in Context
Tengda Han, Max Bain, Arsha Nagrani +3
The objective of this paper is an automatic Audio Description (AD) model that ingests movies and outputs AD in text form. Generating high-quality movie AD is challenging due to the…
VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge
Jaesung Huh, Andrew Brown, Jee-weon Jung +4
This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge…
A CLIP-Hitchhiker's Guide to Long Video Retrieval
Max Bain, Arsha Nagrani, Gül Varol +1
Our goal in this paper is the adaptation of image-text models for long video retrieval. Recent works have demonstrated state-of-the-art performance in video retrieval by adopting C…
VoxSRC 2021: The Third VoxCeleb Speaker Recognition Challenge
Andrew Brown, Jaesung Huh, Joon Son Chung +3
The third instalment of the VoxCeleb Speaker Recognition Challenge was held in conjunction with Interspeech 2021. The aim of this challenge was to assess how well current speaker r…
Audio-Visual Synchronisation in the wild
Honglie Chen, Weidi Xie, Triantafyllos Afouras +3
In this paper, we consider the problem of audio-visual synchronisation applied to videos `in-the-wild' (ie of general classes beyond speech). As a new task, we identify and curate…