25 citations · 65 across the 11 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…
Context-Aware Interleaved Batching for WhisperX
Carlos Bain, Max Bain
While WhisperX accelerates speech transcription via intra-audio batching, it isolates audio segments, losing the historical context needed for coherent punctuation and terminology…
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…
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…
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…
OxfordVGG Submission to the EGO4D AV Transcription Challenge
Jaesung Huh, Max Bain, Andrew Zisserman
This report presents the technical details of our submission on the EGO4D Audio-Visual (AV) Automatic Speech Recognition Challenge 2023 from the OxfordVGG team. We present WhisperX…
Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets
Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall +3
Vision-language models are growing in popularity and public visibility to generate, edit, and caption images at scale; but their outputs can perpetuate and amplify societal biases…
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…
WhisperX: Time-Accurate Speech Transcription of Long-Form Audio
Max Bain, Jaesung Huh, Tengda Han +1
Large-scale, weakly-supervised speech recognition models, such as Whisper, have demonstrated impressive results on speech recognition across domains and languages. However, their a…
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…
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat +4
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness…
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Max Bain, Arsha Nagrani, Gül Varol +1
Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval. The challenges in this area include the design…
Condensed Movies: Story Based Retrieval with Contextual Embeddings
Max Bain, Arsha Nagrani, Andrew Brown +1
Our objective in this work is long range understanding of the narrative structure of movies. Instead of considering the entire movie, we propose to learn from the `key scenes' of t…
Count, Crop and Recognise: Fine-Grained Recognition in the Wild
Max Bain, Arsha Nagrani, Daniel Schofield +1
The goal of this paper is to label all the animal individuals present in every frame of a video. Unlike previous methods that have principally concentrated on labelling face tracks…