Add image-text-to-text pipeline tag, transformers library, and link to paper and project page

This PR adds the `image-text-to-text` pipeline tag and the `transformers` library name to the model card metadata to improve discoverability and clarity. It also adds the project page.

README.md

@@ -1,9 +1,119 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2-VL-7B-Instruct
 language:
 - en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2-VL-7B-Instruct
----
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual Grounding
+
+This is the official repository of **DeepPerception**, an MLLM enhanced with cognitive visual perception capabilities.
+
+[Project Page](https://deepperception-kvg.github.io/)
+
+[Paper](https://arxiv.org/abs/2503.12797)
+
+## Overview
+
+<p align="center">
+    <img src="figs/header.png" width="100%"></a><br>
+    Figure 1: (a) <strong>DeepPerception</strong> employs knowledge-driven reasoning to derive answers, while the baseline model directly outputs predictions without cognitive processing. (b) <strong>DeepPerception</strong> demonstrates superior cognitive visual perception capabilities that cannot be elicited in the foundation model through simplistic zero-shot CoT prompting.
+</p>
+
+#### Abstract
+
+Human experts excel at fine-grained visual discrimination by leveraging domain knowledge to refine perceptual features, a capability that remains underdeveloped in current Multimodal Large Language Models (MLLMs). Despite possessing vast expert-level knowledge, MLLMs struggle to integrate reasoning into visual perception, often generating direct responses without deeper analysis. 
+
+To bridge this gap, we introduce knowledge-intensive visual grounding (KVG), a novel visual grounding task that requires both finegrained perception and domain-specific knowledge integration. To address the challenges of KVG, we propose **DeepPerception**, an MLLM enhanced with cognitive visual perception capabilities. Our approach consists of (1) an automated data synthesis pipeline that generates high-quality, knowledge-aligned training samples, and (2) a two-stage training framework combining supervised fine-tuning for cognitive reasoning scaffolding and reinforcement learning to optimize perceptioncognition synergy. To benchmark performance, we introduce KVG-Bench, a comprehensive dataset spanning 10 domains with 1.3K manually curated test cases. 
+
+Experimental results demonstrate that DeepPerception significantly outperforms direct fine-tuning, achieving +8.08% accuracy improvements on KVG-Bench and exhibiting +4.60% superior cross-domain generalization over baseline approaches. Our findings highlight the importance of integrating cognitive processes into MLLMs for human-like visual perception and open new directions for multimodal reasoning research.
+
+#### Key Contributions
+
+- We introduce the task of **Knowledge-intensive Visual Grounding (KVG)** to explore the concept of cognitive visual perception for MLLMs, aiming to integrate their inherent knowledge and reasoning capabilities into visual perception.
+-  We propose **[DeepPerception](https://huggingface.co/MaxyLee/DeepPerception)**, an MLLM with enhanced cognitive visual perception capabilities. To achieve this, we develop an automated dataset creation pipeline and a two-stage framework integrating supervised cognitive capability enhancement with perception-oriented reinforcement learning.
+- We introduce **[KVG-Bench](https://huggingface.co/datasets/MaxyLee/KVG-Bench)**, a manually curated benchmark for the KVG task involving diverse knowledge domains and entities. Experiments on KVG-Bench and other fine-grained visual recognition tasks demonstrate DeepPerception's exceptional cognitive visual perception capabilities and superior cross-domain generalization performance.
+
+## Get Started
+
+### Contents:
+
+- [Environment](#environment)
+- [Data Preparation](#data-preparation)
+- [Checkpoints](#checkpoints)
+- [Evaluation](#evaluation)
+- [Training](#training)
+
+### Environment
+
+1. Clone this repository and navigate to DeepPerception folder
+```bash
+git clone https://github.com/MaxyLee/DeepPerception.git
+cd DeepPerception
+```
+2. Install Packages
+For evaluation:
+```bash
+conda env create -n deepperception python=3.9
+conda activate deepperception
+
+pip install -r requirements.txt
+```
+
+### Data Preparation
+
+| Dataset                             | Links                                  |
+|---------                            |---------------------------------------|
+| KVG-Bench                           | [`🤗HuggingFace`](https://huggingface.co/datasets/MaxyLee/KVG-Bench)    |
+| KVG Training                        | [`🤗HuggingFace`](https://huggingface.co/datasets/MaxyLee/KVG)    |
+---
+
+### Checkpoints
+
+| Model                               | Links                                  |
+|---------                            |---------------------------------------|
+| DeepPerception                      | [`🤗HuggingFace`](https://huggingface.co/MaxyLee/DeepPerception)    |
+| DeepPerception-FGVR                 | [`🤗HuggingFace`](https://huggingface.co/MaxyLee/DeepPerception-FGVR)    |
+---
+
+### Evaluation
+
+```bash
+# Evaluate on KVG-Bench
+bash eval.sh [CUDA_IDS] [KVG_BENCH_PATH] [CKPT_PATH]
+```
+Notice: Please modify the script if you want to evaluate on Qwen2-VL.
+
+### Training
+
+TODO
+
+## Citation
+
+If you find DeepPerception useful for your research or applications, please cite using this BibTeX:
+
+```bibtex
+@misc{ma2025deepperception,
+      title={DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual Grounding}, 
+      author={Xinyu Ma and Ziyang Ding and Zhicong Luo and Chi Chen and Zonghao Guo and Derek F. Wong and Xiaoyi Feng and Maosong Sun},
+      year={2025},
+      url={https://arxiv.org/abs/2503.12797}, 
+}
+```
+
+## Acknowledgement
+
+- [Qwen2-VL](https://github.com/QwenLM/Qwen2.5-VL)
+- [vLLM](https://github.com/vllm-project/vllm)
+- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- [R1-V](https://github.com/Deep-Agent/R1-V)
+
+## License
+
+[![Code License](https://img.shields.io/badge/Code%20License-MIT-Green.svg)](https://github.com/twbs/bootstrap/blob/main/LICENSE)
+[![Data License](https://img.shields.io/badge/Code%20License-Apache_2.0-Green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)