| license: apache-2.0 | |
| pipeline_tag: image-text-to-text | |
| library_name: transformers | |
| # Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens | |
| This repository hosts a CoVT checkpoint, as presented in the paper [Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens](https://huggingface.co/papers/2511.19418). | |
| **Project Page**: https://wakalsprojectpage.github.io/comt-website | |
| **Code**: https://github.com/Wakals/CoMT | |
| ## Overview of CoVT | |
| Rather than restricting VLM reasoning to a discrete language space with limited representational capacity, **CoVT** forms a visual thought chain that enables VLMs to reason in continuous visual space. By introducing *continuous visual tokens* that encode perceptual cues (e.g., segmentation, depth, instance, and edge structure), CoVT composes *chains of textual and visual thoughts* that link semantic reasoning with perceptual grounding. These visual “thought chains” bridge language and vision, enabling fine-grained understanding, spatial precision, and geometric awareness beyond the reach of text-based reasoning. | |
| ## CoVT Checkpoint (Depth Aligned) | |
| This CoVT checkpoint is aligned with **4 Depth tokens**. | |
| These task-specific tokens are integrated into the model’s embedding space to enhance depth-awareness. |