There will be an Art Gallery at #ECCV2026 😍

Submit your artwork made with or about computer vision by 14th June

Details: eccv.ecva.net/Conferences/...

Co-organising with @ysiglidis.bsky.social

🖼️ @zzznah.bsky.social @mattierial.bsky.social @annaridler.bsky.social from #ECCV2018

@eccv.bsky.social

Thanks! It was a tough one too

GenPT's generative capabilities can be leveraged to improve point trajectory estimates by utilizing a best-first search on generated samples during inference, guided by the model’s own confidence of its predictions.

GenPT is trained with a modified flow matching setup, incorporating iterative refinements, a window-dependent prior for cross-window consistency, and a variance schedule tuned for point coordinates.

It is a generative framework for modelling multi-modal point trajectories.

Tracking the location of a point through a video is an ill-posed problem, where multiple plausible solutions can arise during visual obfuscations, such as occlusion.

Current point trackers are discriminative and fail to model the multi-modality inherent in point tracking.

Generative Point Tracking with Flow Matching

My latest project with Adam W. Harley, @csprofkgd.bsky.social, Derek Nowrouzezahrai, @chrisjpal.bsky.social.

Project page: mtesfaldet.net/genpt_projpa...
Paper: arxiv.org/abs/2510.20951
Code: github.com/tesfaldet/ge...

I just pushed a new paper to arXiv. I realized that a lot of my previous work on robust losses and nerf-y things was dancing around something simpler: a slight tweak to the classic Box-Cox power transform that makes it much more useful and stable. It's this f(x, λ) here:

Please tell people that they don’t have to stand out to do good work and be respected.

Doing honest research and taking care of your colleagues and your field can be as valuable.

We already have so much anxiety and fomo in the field, I’m not sure it’s making us better.

A new study finds ants best humans at tests of collective intelligence.

Learn more: scim.ag/4h2K0ID

Uhhh wat

I think getting into any PhD for lucrativeness hasn’t really ever been a good idea. Personally, the challenge I’ve been facing with my AI PhD has been how industry competition has negatively affected the ability to publish. Resource requirements are at an all time high for most types of projects.

I wouldn’t pivot just for pivot’s sake though. If you feel that you’re enjoying the direction you’re taking, then stick with it. That being said, if you feel stuck in the mud for a couple of years, then it might be best to pivot.

feeling a but under the weather this week … thus an increased level of activity on social media and blog: kyunghyuncho.me/i-sensed-anx...

I feel seen. I’m going on to my sixth year and it’s been an immense struggle. I’ve missed many opportunities due to not working on LLMs, and many more due to working on self-organizing models. So I decided to pivot to a more traditional vision subject: point tracking. It’s been a demoralizing PhD…

I'm doing another AI Art Gallery with @cvprconference.bsky.social in Nashville this year 😍🤖🎨

Submissions for artworks using / about computer vision are open until 9th March 2025

More details: bit.ly/CVPRAIArt25

#CVPR2025 #creativeAI #AIart

I’m going to guess about 2 TB worth of keynote files.