Add link to paper and update task categories

README.md

@@ -1,32 +1,36 @@
 ---
 language:
-  - en
-tags:
-  - remote-sensing
-  - image-quality-assessment
-  - benchmark
-  - visual-question-answering
+- en
+license: cc-by-4.0
 task_categories:
-  - visual-question-answering
-  - image-classification
+- image-text-to-text
+- visual-question-answering
 pretty_name: SenseBench
-license: cc-by-4.0
+tags:
+- remote-sensing
+- image-quality-assessment
+- benchmark
 ---
 
 # SenseBench
 
-> A benchmark for remote sensing low-level visual perception and description in large vision-language models.
+[**SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models**](https://huggingface.co/papers/2605.10576)
 
-🏠 [github](https://github.com/Zhong-Chenchen/SenseBench) | 🤗 [Hugging Face Fullset](https://huggingface.co/datasets/Zhongchenchen/SenseBench)
+🏠 [GitHub](https://github.com/Zhong-Chenchen/SenseBench) | 🤗 [Hugging Face Fullset](https://huggingface.co/datasets/Zhongchenchen/SenseBench)
 
 ## Overview
 
-SenseBench is a remote sensing benchmark for evaluating low-level visual perception and description in large vision-language models.
+SenseBench is the first dedicated diagnostic benchmark for remote sensing (RS) low-level visual perception and description. Driven by a physics-based hierarchical taxonomy, SenseBench features over 10K meticulously curated instances across 6 major and 22 fine-grained RS degradation categories. 
+
+The benchmark evaluates Vision-Language Models (VLMs) through two complementary protocols:
+- **SensePerception**: Objective low-level visual perception (using What/Whether/How questions).
+- **SenseDescription**: Subjective diagnostic description focusing on completeness, correctness, and faithfulness.
 
 ## Supported Tasks
 
-- Visual question answering
-- Text generation
+- Visual Question Answering
+- Image-to-Text / Text Generation
+- Image Quality Assessment
 
 ## Language
 
@@ -43,7 +47,10 @@ Each example contains image paths, a question, an answer, and metadata describin
     "images/4fda312e-70d2-4df7-b1f7-2f06955bf338_0.png",
     "images/4fda312e-70d2-4df7-b1f7-2f06955bf338_1.png"
   ],
-  "question": "Using the options provided, rate the overall quality of Image 2 compared to Image 1.\nA.No/Slight distortion\nB.Moderate distortion\nC.Severe distortion",
+  "question": "Using the options provided, rate the overall quality of Image 2 compared to Image 1.
+A.No/Slight distortion
+B.Moderate distortion
+C.Severe distortion",
   "answer": "A",
   "meta": {
     "image_count": "multi",
@@ -57,3 +64,16 @@ Each example contains image paths, a question, an answer, and metadata describin
   }
 }
 ```
+
+## Citation
+
+If you use SenseBench in your research, please cite:
+
+```bibtex
+@article{zhong2026sensebench,
+  title={SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models},
+  author={Zhong, Chenchen and others},
+  journal={arXiv preprint arXiv:2605.10576},
+  year={2026}
+}
+```