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🚀 New Paper: Pixel Reasoner 🧠🖼️ How can Vision-Language Models (VLMs) perform chain-of-thought reasoning within the image itself? We introduce Pixel Reasoner, the first open-source framework that enables VLMs to “think in pixel space” through curiosity-driven reinforcement learning. Current VLMs reason only in text — even when grounded in...

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Wenhu Chen

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82,829 просмотров • 1 год назад •via X (Twitter)

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Фото профиля Wenhu Chen
Wenhu Chen1 год назад

We found that using standard GRPO does not work well because VLMs tend to ignore these visual operations. Therefore, we propose the curiosity-driven reward to incentivize the model to use visual operations properly but not over-use it. RaPR is the ratio of rollouts in one group that use visual operations. 1_{PR} means whether a specific rollout uses visual operations. H is a threashhold. So the r_curiosity will reward the individual rollout in the groups which have low visual operation rate. r_penalty will penalize the over-use of the visual operations to prevent reward hacking. This reward design is the key to build Pixel Reasoner.

Фото профиля Wenhu Chen
Wenhu Chen1 год назад

Great work led by Alex Su and Haozhe Wang, in collaboration with HKUST and USTC.

Фото профиля Rainmaker
Rainmaker2 лет назад

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Фото профиля Chengzu Li
Chengzu Li1 год назад

Very cool work! We are also exploring reasoning with image, but through image generation as imagination. If you are interested, feel free to take a look!

Фото профиля Quizmaster China
Quizmaster China1 год назад

Congratulations! 加油!

Фото профиля Awsaf
Awsaf1 год назад

Wow. Here's another o3 inspired work:

Фото профиля 🙉🙉
🙉🙉1 год назад

Very cool!

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11,786 просмотров • 11 месяцев назад

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AK

250,112 просмотров • 3 лет назад

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Some food for thought when designing benchmarks... So, here's a little computer-use (visual) comparison between GPT-5.6 Astra and Qwen3.8 Max. The task here was to recreate the image in the center using the Paint UI. Super interesting how the two different LLMs+Harnesses approached this totally differently by default. I.e., Astra tried to approach this by drawing and layering geometric shapes. Qwen approached this pixel by pixel. (Of course, the pixel-by-pixel result looks closer to the original, it's essentially a low-res version of that by nature.) So, the Qwen-generated image would surely score higher in the sense that it's closer to the original. But I wouldn’t conclude from this example that one LLM generalizes better than the other on other tasks. Also, I wouldn't say Qwen has better compute-use capabilities or better visual understanding than Astra. But it highlights an interesting point about how slippery benchmarks are when they only compare final results.

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Image-to-Pixel 3.0 is out 🎨 Turn images and video into pixel art right in your browser. Free, MIT open source. Try it: Or use it in code: npm i image-to-pixel

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TokenFlow: Consistent Diffusion Features for Consistent Video Editing paper page: The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos.

AK

143,557 просмотров • 3 лет назад

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Sakana AI

179,465 просмотров • 1 год назад

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35,606 просмотров • 2 лет назад

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Excited to share our new work: StreamingVLM! 🚀 We tackle a major challenge for Vision-Language Models (VLMs): understanding infinite video streams in real-time without latency blowing up or running out of memory. Paper: Code:

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