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README.md

@@ -12,17 +12,17 @@ arxiv: 2506.08915
 library_name: generic
 ---
 
-# IFAM (metashift-k8) Model Checkpoint
+# iFAM (metashift-k8) Model Checkpoint
 
-This is the official pre-trained checkpoint of the **IFAM (Iterative Focus and Attention Masking)** framework, proposed in the paper **"Two-stage Vision Transformers and Hard Masking offer Robust Object Representations"** (accepted as an oral presentation at ICPR 2026).
+This is the official pre-trained checkpoint of the **iFAM (Inherently Faithful Attention Maps for Vision Transformers)** framework, proposed in the paper **"Two-stage Vision Transformers and Hard Masking offer Robust Object Representations"** (accepted as an oral presentation at ICPR 2026).
 
 - **Paper:** [Two-stage Vision Transformers and Hard Masking offer Robust Object Representations](https://arxiv.org/abs/2506.08915)
 - **Repository:** [GitHub - ananthu-aniraj/ifam](https://github.com/ananthu-aniraj/ifam)
 
 ## Model Description
-IFAM model trained on the Metashifts dataset with 8 parts (K=8).
+iFAM model trained on the Metashifts dataset with 8 parts (K=8).
 
-The IFAM framework is a two-stage approach:
+The iFAM framework is a two-stage approach:
 1. **Stage 1 (Selector):** Processes the full image to discover object parts and identify task-relevant regions.
 2. **Stage 2 (Predictor):** Restricts its receptive field to the selected regions using input attention masking, preventing spurious background details from affecting the classification.