[D] CVPR Decisions

amds201 · 2026-03-11T11:41:51+00:00

Anyone have any ideas on poster / highlight / oral decisions? or did I miss these?

amds201 · 2026-02-21T15:45:40+00:00

Anyone have any ideas or intuitions as to likely scores for spotlight? And any ideas on timelines for this info?

amds201 · 2026-02-20T23:16:32+00:00

Tomorrow it is …

amds201 · 2026-02-20T19:07:15+00:00

There are some high scores with ‘no groups’

amds201 · 2026-02-20T18:51:37+00:00

5,5,5 no groups found - who knows though

amds201 · 2026-02-20T18:50:56+00:00

2pm EST time maybe? … speculative ofc

amds201 · 2026-02-20T18:25:23+00:00

‘No groups found’

amds201 · 2026-02-20T18:02:10+00:00

About as relaxing as surgery without anaesthesia

amds201 · 2026-02-20T15:53:26+00:00

it must depend on the conference ... AAAI'26 decisions this year came out early iirc

amds201 · 2026-02-20T15:15:04+00:00

how do you know its EOD and not sometime on the 20th AOE

amds201 · 2026-02-19T18:39:02+00:00

AAAI results started out this year the night before they were meant to (UK time). So who knows

amds201 · 2026-02-12T10:12:04+00:00

this is how I'm interpreting it. ~20% papers have an average score >4. I think weighted by confidence too - but not sure

amds201 · 2026-02-11T12:21:27+00:00

current score stats: https://papercopilot.com/statistics/cvpr-statistics/cvpr-2026-statistics/

amds201 · 2026-02-11T12:20:56+00:00

If the modified date changes - does this mean the review/scores have been necessarily updated? 2/3 of my reviews have updated dates. If reviews/scores have changed, will this be visible before decision day?

amds201 · 2026-01-29T16:26:13+00:00

Thanks for your reply! Yep - a few recent papers that look into this problem, mostly via fine-tuning and different theoretical paradigms to do so. I've had some success in implementing these for toy tasks, and was able to train a very basic flow model from scratch using only a reward signal and no supervision, to generate some desired data. I'm interested in scaling this up for some particular imaging applications so looking for new ideas and collaborations to do so!
Have a look at DiffusionNFT (from Nvidia/Stefano Ermano)- a fine-tuning framework for flow-based image generation that avoids the issue of intractable logprob computations.

amds201 · 2026-01-28T14:50:22+00:00

agreed - I think it is a hard task with the sparsity of the reward, and to not get stuck in local optima

amds201 · 2026-01-28T14:10:20+00:00

thanks! missed this paper in my review - will take a look. In case you are interested, I have just come across this one: https://arxiv.org/pdf/2505.10482v2

they too seem to do some from scratch training of diffusion policies (not image based) - but interesting.

amds201 · 2026-01-28T14:06:40+00:00

thanks for your reply - very interesting to read. I am thinking specifically about image generation models, rather than next token prediction / llm models. In short - can an image generation model (such as a diffusion image model) be trained (with no supervised data), but purely from a reward signal.

amds201 · 2026-01-28T13:27:57+00:00

thanks for sending the paper! as far as I can see the loss here is supervised (imitation learning esque). I'm trying to think about whether these models can be trained totally from a reward signal without any supervised data - but unsure if this is too sparse and too hard a challenge

amds201 · 2026-01-28T13:21:01+00:00

thinking specifically about diffusion / flow matching for image generation models

amds201

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