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Our Video-RB obtain strong performance on several video reasoning benchmarks. Besides, although the model is trained using only 16 frames, we find that evaluating on more frames e. The script for training the obtained Qwen2.

Troubleshoot YouTube video errors : Est

After applying basic rule-based filtering to remove low-quality or inconsistent outputs, we obtain a high-quality CoT dataset, Video-R1-COT k. For efficiency considerations, we limit the maximum number of video frames to 16 during training. Open-Sora Plan: Open-Source Large Video Generation Model.

We guess this is because the model initially discards its previous, potentially sub-optimal reasoning style. You signed in with another tab or window. Some examples are as follows. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the.

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-RB achieves a new state-of-the-art accuracy of %, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters.

This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the effectiveness of reinforcement learning for video tasks. We sincerely appreciate the contributions of the open-source community. To facilitate an effective SFT cold start, we leverage Qwen2.

Skip to content. Video-R1 significantly outperforms previous models across most benchmarks. 💡 I also have other video-language projects that may interest you. These results indicate the importance of training models to reason over more frames.

Please reload this page. There was an error while loading. The Video-Rk. Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth. Then gradually converges to a better and stable reasoning policy.

EMNLP 2024 Video LLaVA : It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities

We collect data from a variety of public datasets and carefully sample and balance the proportion of each subset. ByteDance †Corresponding author This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability.

For example, Video-RB attains a To overcome the scarcity of high-quality video reasoning training data, we strategically introduce image-based reasoning data as part of training data. Our code is compatible with the following version, please download at here.

Due to current computational resource limitations, we train the model for only 1. The accuracy reward exhibits a generally upward trend, indicating that the model continuously improves its ability to produce correct answers under RL.

Interestingly, the response length curve first drops at the beginning of RL training, then gradually increases. For all evaluations, we follow the decoding configuration used in the official Qwen2. Video-R1 significantly outperforms previous models across most benchmarks.