Update dataset card: Add task categories

README.md

@@ -1,12 +1,11 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
-base_model:
-  - Qwen/Qwen2.5-VL-3B-Instruct
-  - Qwen/Qwen2.5-VL-7B-Instruct
-pipeline_tag:  image-text-to-text
+license: apache-2.0
+task_categories:
+- object-detection
+- image-segmentation
 ---
 
 <div align='center'><h1>Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs</h1></div>
@@ -30,25 +29,6 @@ By introducing VRTs, we achieve **semantic reasoning and object-specific visual
 
 As illustrated in Figure C, we have validated PaDT across four major visual perception and understanding tasks. In all cases, PaDT achieves **state-of-the-art** performance compared to conventional character-by-character coordinate-generation MLLMs.
 
-We hope this work will inspire further exploration in the community:
-
-- What does true multimodal reasoning look like?
-
-- How can textual and visual elements be generated together in an MLLM output sequence?
-
-- And is a purely text-based output ever sufficient for visual reasoning?
-
-<div align="center">
-<img src="./assets/Motivation.webp" width="900"/>
-<p>Figure B. Some observations on conventional character-by-character coordinate-generation MLLMs and our PaDT.</p>
-</div>
-
-
-<div align="center">
-<img src="./assets/TaskIntroduction.webp" width="900"/>
-<p>Figure C. PaDT works on four visual perception and understanding tasks.</p>
-</div>
-
 ## Quick Start
 
 Clone this repo, and set up the environment with a few commands.
@@ -122,7 +102,8 @@ with torch.inference_mode():
     # extract Visual Reference Tokens within the sequence
     completions, feats, labels, vrts, vrts_feats = parseVRTintoCompletion(processor, completion_ids, generate_returned_result['hidden_states'], torch.Tensor([False]))
 
-    print("\ngenerate result:", completions[0])
+    print("
+generate result:", completions[0])
 
     # decode low-level visual task results
     low_res_image_embeds = generate_returned_result.past_image_embeds
@@ -130,7 +111,10 @@ with torch.inference_mode():
     visual_pe = generate_returned_result.past_visual_pe
     decoded_list = model.vl_decode(feats, low_res_image_embeds, high_res_image_embeds, prompt_inputs['image_grid_thw'], visual_pe)
 
-    print(f"\npred_bboxes: {decoded_list['pred_boxes']},\npred_scores: {decoded_list['pred_score'].sigmoid()}\n")
+    print(f"
+pred_bboxes: {decoded_list['pred_boxes']},
+pred_scores: {decoded_list['pred_score'].sigmoid()}
+")
 ```
 
 ## Models
@@ -192,4 +176,4 @@ We kindly encourage citation of our work if you find it useful.
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2510.01954}, 
 }
-```
+```