Get to know @hazeldoughty.bsky.social, Asst Prof at @unileiden.bsky.social 🇳🇱 and ELLIS Member. She researches computer vision, with a focus on fine-grained video understanding under limited supervision. Her goal is to enable a deeper understanding of video content and motion. #WomenInELLIS

Curious about turning coarse video classes into fine-grained ones without retraining or video data? If you’re at #ICLR2026, come check out our poster 📅 Friday 24th April, 15:15-17:45📍Pavillion 4, poster #3715 arxiv.org/abs/2602.16545

Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding

Video recognition models are typically trained on fixed taxonomies which are often too coarse, collapsing distinctions in object, manner or outcome under a single label. As tasks and definitions evolv...

arxiv.org

We’re organizing the 5th DataCV Workshop @ #CVPR2026 . If your work focuses on data, such as bias, robustness, distribution shifts, synthetic data, or dataset analysis, we’d love to see it. Proceedings + DataCV Challenge. Deadline: March 10, 2026 (AOE) sites.google.com/view/datacv-...

DataCV 2026 @ CVPR 2026

Announcements The 5th DataCV Workshop and Challenge will be held as a half-day workshop in Denver, Colorado, USA, in conjunction with CVPR 2026. Paper submission deadline: March 10th, 2026 (23:59 Any...

sites.google.com

How flexible is a trained video classifier after training? Our new #ICLR2026 paper investigates whether a category can be split into finer ones without retraining and without any videos. arxiv.org/abs/2602.16545

Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding

Video recognition models are typically trained on fixed taxonomies which are often too coarse, collapsing distinctions in object, manner or outcome under a single label. As tasks and definitions evolv...

arxiv.org

Excited about detailed visual reasoning and subtle distinctions in #ComputerVision? Only 1 week left to apply 👇

Hazel Doughty@hazeldoughty.bsky.social · 7mo ago

✨PhD vacancy alert✨ Joost Batenburg and I are looking for someone that wants to work on fine-grained visual understanding in #ComputerVision Apply here before 20 Feb: careers.universiteitleiden.nl/job/PhD-Cand...

🏹 Job alert: PhD Candidate in Fine-Grained Visual Understanding at @unileiden.bsky.social 📍 Leiden 🇳🇱 📅 Apply by Feb 20th 🔗 https://careers.universiteitleiden.nl/job/PhD-Candidate-in-Fine-Grained-Visual-Understanding/16323-en_US/

✨PhD vacancy alert✨ Joost Batenburg and I are looking for someone that wants to work on fine-grained visual understanding in #ComputerVision Apply here before 20 Feb: careers.universiteitleiden.nl/job/PhD-Cand...

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

Dima Damen@dimadamen.bsky.social · 2y ago

🛑📢 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

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.

Hazel Doughty@hazeldoughty.bsky.social · 2y ago

VQA Benchmark Our benchmark tests understanding in recipes, ingredients, nutrition, fine-grained actions, 3D perception, object movement and gaze. Current models have a long way to go with a best performance of 38% vs. 90% human baseline.

#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 👌

Hazel Doughty@hazeldoughty.bsky.social · 2y ago

📢 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.

📢 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