Tomorrow, I’ll give a talk about future predictions in egocentric vision at the #CVPR2025 precognition workshop, in room 107A at 4pm. I’ll retrace some history and show how precognition enables assistive downstream tasks and representation learning for procedural understanding.

Excited to be giving a keynote at the #CVPR2025 Workshop on Interactive Video Search and Exploration (IViSE) tomorrow. I'll be sharing our efforts working towards detailed video understanding. 📅 09:45 Thursday 12th June 📍 208 A 👉 sites.google.com/view/ivise2025

Have you heard about HD-EPIC? Attending #CVPR2025 Multiple opportunities to know about the most highly-detailed video dataset with a digital twin, long-term object tracks, VQA,… hd-epic.github.io 1. Find any of the 10 authors attending @cvprconference.bsky.social – identified by this badge. 🧵

Do you want to prove your Video-Language Model understands fine-grained, long-video, 3D world or anticipates interactions? Be the 🥇st to win HD-EPIC VQA challenge hd-epic.github.io/index#vqa-be... DL 19 May Winners announced @cvprconference.bsky.social #EgoVis workshop

Object masks &tracks for HD-EPIC have been released.. This completes our highly-detailed annotations. Also, HD-EPIC VQA challenge is open [Leaderboard closes 19 May]... can you be 1st winner? codalab.lisn.upsaclay.fr/competitions... Btw, HD-EPIC was accepted @cvprconference.bsky.social #CVPR2025

The HD-EPIC VQA challenge for CVPR 2025 is now live: codalab.lisn.upsaclay.fr/competitions... See how your model stacks up against Gemini and LLaVA Video on a wide range of video understanding tasks.

#CVPR2025 PRO TIP: To get a discount on your registration, join the Computer Vision Foundation (CVF). It’s FREE and makes @wjscheirer smile 😉 CVF: thecvf.com

HD-EPIC - hd-epic.github.io Egocentric videos 👩‍🍳 with very rich annotations: the perfect testbed for many egocentric vision tasks 👌

📢 Today we're releasing a new highly detailed dataset for video understanding: HD-EPIC arxiv.org/abs/2502.04144 hd-epic.github.io What makes the dataset unique is the vast detail contained in the annotations with 263 annotations per minute over 41 hours of video.

🛑📢 HD-EPIC: A Highly-Detailed Egocentric Video Dataset hd-epic.github.io arxiv.org/abs/2502.04144 New collected videos 263 annotations/min: recipe, nutrition, actions, sounds, 3D object movement &fixture associations, masks. 26K VQA benchmark to challenge current VLMs 1/N

Our second #ACCV2024 oral: "Beyond Coarse-Grained Matching in Video-Text Retrieval" is also being presented today. ArXiv: arxiv.org/abs/2410.12407 We go beyond coarse-grained retrieval and explore whether models can discern subtle single-word differences in captions.

Today we're presenting out #ACCV2024 Oral "LocoMotion: Learning Motion-Focused Video-Language Representations". We remove the spatial focus of video-language representations and instead train representations to have a motion focus.

Pls RT Permanent Assistant Professor (Lecturer) position in Computer Vision @bristoluni.bsky.social [DL 6 Jan 2025] This is a research+teaching permanent post within MaVi group uob-mavi.github.io in Computer Science. Suitable for strong postdocs or exceptional PhD graduates. t.co/k7sRRyfx9o 1/2