Update README.md

README.md

@@ -108,7 +108,7 @@ The ImageNet-D dataset was constructed using diffusion models to generate a larg
 
    For example, to generate an image of a backpack, the prompt might specify "a backpack in a wheat field" to control both the object category and background nuisance.
 
-2. **Prompt design**: A set of prompts was carefully designed to cover a matrix of object categories and nuisance attributes (see Table 1 in the paper for an overview). This allows generating images with a much broader range of category-nuisance combinations compared to existing test sets.
+2. **Prompt design**: A set of prompts was carefully designed to cover a matrix of object categories and nuisance attributes (see [Table 1 in the paper](https://arxiv.org/html/2403.18775v1#S3) for an overview). This allows generating images with a much broader range of category-nuisance combinations compared to existing test sets.
 
 3. **Labeling**: Each generated image is automatically labeled with the object category (C) specified in its generation prompt. This category label serves as the ground truth for evaluating classification models on the ImageNet-D dataset. A classification is considered incorrect if the model's predicted class does not match the ground truth category.
 
@@ -118,9 +118,11 @@ Chenshuang Zhang, Fei Pan, Junmo Kim, In So Kweon, Chengzhi Mao
 
 ## Citation 
 **BibTeX:**
+```bibtex
 @article{zhang2024imagenet_d,
   author    = {Zhang, Chenshuang and Pan, Fei and Kim, Junmo and Kweon, In So and Mao, Chengzhi},
   title     = {ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object},
   journal   = {CVPR},
   year      = {2024},
-}
+}
+```