Max Bain
Max Bain is a computer vision researcher whose work helps AI systems understand video, speech and language. He coauthored methods for identifying individual chimpanzees in video and datasets for searching films and videos with text. His Oxford research used audio and video to recognise wild chimpanzee behaviour; AutoAD generated movie audio descriptions, and WhisperX added word-level timestamps to long-form speech. After coauthoring multimodal models and a visual-understanding benchmark at Reka, he is a Research Scientist at Google DeepMind. He coauthored the Gemini 2.5 report in 2025, improved WhisperX in 2026 and coauthored a chimpanzee video dataset in 2026.
- Now at
- Google DeepMind
- Works
- 30
- Citations
- 783
- h-index
- 11
- Badges
Publication numbers from OpenAlex, as of 22 October 2026.
Timeline
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2019
Chimpanzee recognition from video
In 2019, he coauthors Count, Crop and Recognise, a multi-stage method for identifying chimpanzee individuals in video frames. The paper introduces a dataset for chimpanzee recognition in the wild.
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2020
The Condensed Movies dataset
In 2020, he coauthors Condensed Movies with Arsha Nagrani, Andrew Brown and Andrew Zisserman. The dataset uses key scenes from over 3,000 films for story-based retrieval, and the paper appears as an oral presentation at ACCV 2020.
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2021
Frozen in Time and WebVid-2M
With Arsha Nagrani, Gül Varol and Andrew Zisserman, he coauthors Frozen in Time in 2021, a joint video and image encoder for text-to-video retrieval. The paper introduces WebVid-2M, a pretraining dataset of more than two million videos with weak captions scraped from the internet.
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November 2021
Wild chimpanzee behaviour recognition
As a DPhil student in Oxford's Visual Geometry Group supervised by Andrew Zisserman, he helps develop AI models that recognise wild chimpanzee behaviour from audio and video; the study appears in Science Advances in November 2021. The models use footage from Bossou in Guinea and Cantanhez National Park in Guinea-Bissau to detect nut-cracking, eating and buttress drumming.
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March 2023
WhisperX for long-form speech
With Jaesung Huh, Tengda Han and Andrew Zisserman, he introduces WhisperX in March 2023, a speech recognition system that uses voice activity detection and forced phoneme alignment to add word-level timestamps. Accepted to INTERSPEECH 2023, it ranks first in the EGO4D Audio-Visual Automatic Speech Recognition Challenge, and the team makes the baseline code and models available online.
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2023
AutoAD and an Oxford DPhil
In 2023, he coauthors AutoAD, a model that ingests movies and writes audio descriptions as text; the paper is a CVPR 2023 Highlight. His 2023 Oxford DPhil thesis, Understanding Video Through the Lens of Language, is supervised by Andrew Zisserman and explores audio description for visually impaired audiences.
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2024
Multimodal work at Reka
At Reka, where he serves as a Member of Technical Staff, he coauthors the April 2024 report on Core, Flash and Edge. The models are trained from scratch to process text, images, video and audio; in May 2024, he coauthors Vibe-Eval, a benchmark with 269 visual-understanding prompts for multimodal chat models.
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As of October 2026
Research Scientist at Google DeepMind
In July 2025, he is among the authors of the Gemini 2.5 technical report. In August 2026, he and Carlos Bain publish context-aware interleaved batching for WhisperX, which reduces word error rate while maintaining high throughput; in October 2026, he coauthors BossouChimpanzee, a video dataset of wild chimpanzees at Bossou, Guinea, spanning 1988 to 2018. As of October 2026, he is a Research Scientist at Google DeepMind.
Career
- Since 2025Google DeepMindResearch scientist
- Until 2024Reka AIMember of technical staff
Education
- From 2021University of OxfordDPhil
Most-cited works
- WhisperX: Time-Accurate Speech Transcription of Long-Form AudioWith Andrew Zisserman
- Automated audiovisual behavior recognition in wild primatesWith Andrew Zisserman
- Frozen in Time: A Joint Video and Image Encoder for End-to-End RetrievalWith Andrew Zisserman
- AutoAD: Movie Description in ContextWith Andrew Zisserman
- A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Sources and credits
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- Count, Crop and Recognise: Fine-Grained Recognition in the Wild
- [2005.04208] Condensed Movies: Story Based Retrieval with Contextual Embeddings
- [2104.00650] Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
- Artificial intelligence used to recognise primate behaviours in the wild
- [2303.00747] WhisperX: Time-Accurate Speech Transcription of Long-Form Audio
- [2307.09006] OxfordVGG Submission to the EGO4D AV Transcription Challenge
- [2303.16899] AutoAD: Movie Description in Context
- Understanding video through the lens of language - ORA - Oxford University Research Archive
- Max Bain
- [2404.12387] Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
- [2405.02287] Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
- [2507.06261] Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
- [2608.31170] Context-Aware Interleaved Batching for WhisperX
- [2610.05293] BossouChimpanzee: Long-term Chimpanzee Video Dataset


