Add link to paper and GitHub repository

This PR updates the dataset card with links to the original paper and the official GitHub repository. It also adds the `image-text-to-text` task category to the YAML metadata to reflect the benchmark's diagnostic description tasks for Vision-Language Models.

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
+- image-classification
 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.
 
-🏠 [github](https://github.com/Zhong-Chenchen/SenseBench) | 🤗 [Hugging Face Subset](https://huggingface.co/datasets/Zhongchenchen/SenseBench_subset)
+🏠 [GitHub](https://github.com/Zhong-Chenchen/SenseBench) | 📄 [Paper](https://huggingface.co/papers/2605.10576) | 🤗 [Hugging Face Subset](https://huggingface.co/datasets/Zhongchenchen/SenseBench_subset)
 
 ## 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, it features over 10K curated instances across 6 major and 22 fine-grained RS degradation categories. It is designed to evaluate whether Vision-Language Models (VLMs) can overcome the domain gap to perceive and articulate RS-specific artifacts.
+
+The benchmark evaluation consists of two complementary protocols:
+1. **Objective low-level visual perception**: Evaluating the model's ability to identify the presence and type of distortions.
+2. **Subjective diagnostic description**: Evaluating the model's ability to articulate RS artifacts in natural language based on completeness, correctness, and faithfulness.
 
 ## Supported Tasks
 
-- Visual question answering
-- Text generation
+- **Visual Question Answering**: Multiple-choice questions assessing degradation type and severity.
+- **Image-to-Text / Diagnostic Description**: Natural language generation describing visual artifacts.
 
 ## 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",
@@ -56,4 +63,4 @@ Each example contains image paths, a question, an answer, and metadata describin
     "comparison": "intra-image"
   }
 }
-```
+```