taesiri

5 ms·

1.
SketchVLM: Letting VLMs draw on images while explaining their reasoning(github.com)
3 points by taesiri 5mo ago|1 comments
2.
by taesiri 5mo ago
would be sick with meta glasses; just look at broken things, draws what you mean, and get help fixing it. not just fixing but anything
3.
iPhone-17e(apple.com)
2 points by taesiri 7mo ago|0 comments
4.
Compilation of physical glitches in Sora 2(twitter.com)
1 points by taesiri 1y ago|0 comments
5.
Glitches in Sora 2 World(old.reddit.com)
2 points by taesiri 1y ago|0 comments
6.
by taesiri 1y ago
for overly represented concepts, like popular brands, it seems that the model “ignores” the details once it detects that the overall shapes or patterns are similar. Opening up the vision encoders to find out how these images cluster in the
7.
Vision Language Models Are Biased(vlmsarebiased.github.io)
176 points by taesiri 1y ago|141 comments
8.
by taesiri 1y ago
State-of-the-art Vision Language Models achieve 100% accuracy counting on images of popular subjects (e.g. knowing that the Adidas logo has 3 stripes and a dog has 4 legs) but are only ~17% accurate in counting in counterfactual images (e.g
9.
LLMs can reduce their bias in multi-turn conversations(b-score.github.io)
5 points by taesiri 1y ago|0 comments
10.
by taesiri 1y ago
tldr; We find that GenAI can satisfy 1/3 of everyday image editing requests, while 2/3 of the requests are better handled by human image editors.
11.
Understanding Generative AI Capabilities in Everyday Image Editing Tasks(arxiv.org)
5 points by taesiri 1y ago|1 comments
12.
Hot prompting boosts LLM accuracy with fact highlights(twitter.com)
4 points by taesiri 2y ago|0 comments
13.
Hot: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs(arxiv.org)
4 points by taesiri 2y ago|1 comments
14.
by taesiri 2y ago
Abstract: An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate non-factual statements. A response mixed of factual and non-factual statements poses a challenge for humans to verify and accurately base their deci
15.
by taesiri 2y ago
Abstract: Large Multimodal Models (LMMs) exhibit major shortfalls when interpreting images and, by some measures, have poorer spatial cognition than small children or animals. Despite this, they attain high scores on many popular visual ben
16.
ZeroBench: An Impossible Visual Benchmark for Contemporary LMMs(arxiv.org)
9 points by taesiri 2y ago|3 comments
17.
by taesiri 2y ago
All frontier models, (o1, o1-pro, QVQ, gemini-flash-thinking) score exactly 0% on main questions of this benchmark.
18.
Vision language models are blind(vlmsareblind.github.io)
451 points by taesiri 2y ago|191 comments
19.
by taesiri 2y ago
This paper examines the limitations of current vision-based language models, such as GPT-4 and Sonnet 3.5, in performing low-level vision tasks. Despite their high scores on numerous multimodal benchmarks, these models often fail on very ba
20.
by taesiri 3y ago
Not one place, but there are some people tweeting about new papers daily (@arankomatsuzaki, @_akhaliq, @omarsar0) other people summarizing papers (@davisblalock, @rasbt). Latent Space podcast is also great and of course r/LocalLLaMA&#
21.
by taesiri 3y ago
Coool! Would be nice to have an option to send commands to an LLM and show the results to the "user"! :D
22.
Planaritify(play.google.com)
1 points by taesiri 8y ago|0 comments
23.
The Internet in IRAN sucks – but does it?(medium.com)
5 points by taesiri 8y ago|0 comments
24.
Worst game to test Memory (iOS)(itunes.apple.com)
2 points by taesiri 9y ago|0 comments
25.
Planaritify – My iOS game about planar graphs, made in one day(itunes.apple.com)
6 points by taesiri 9y ago|1 comments