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
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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
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