Cover Image

GMAR: Gradient-Driven Multi-Head Attention Rollout for Vision Transformer Interpretability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jo, Sehyeong, Jang, Gangjae, Park, Haesol
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Decision-Aware Attention Propagation for Vision Transformer Explainability
by: Jo, Sehyeong, et al.
Published: (2026)

Improving Vision Transformers by Overlapping Heads in Multi-Head Self-Attention
by: Zhang, Tianxiao, et al.
Published: (2024)

ComFe: An Interpretable Head for Vision Transformers
by: Mannix, Evelyn J., et al.
Published: (2024)

MAIR++: Improving Multi-view Attention Inverse Rendering with Implicit Lighting Representation
by: Choi, JunYong, et al.
Published: (2024)

Multi-manifold Attention for Vision Transformers
by: Konstantinidis, Dimitrios, et al.
Published: (2022)

Interpreting Attention Heads for Image-to-Text Information Flow in Large Vision-Language Models
by: Kim, Jinyeong, et al.
Published: (2025)

MHAFF: Multi-Head Attention Feature Fusion of CNN and Transformer for Cattle Identification
by: Dulal, Rabin, et al.
Published: (2025)

Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads
by: Yeo, Wei Jie, et al.
Published: (2025)

Optimizing Knowledge Distillation in Transformers: Enabling Multi-Head Attention without Alignment Barriers
by: Bing, Zhaodong, et al.
Published: (2025)

Interpretability-Aware Vision Transformer
by: Qiang, Yao, et al.
Published: (2023)

Sparse but not Simpler: A Multi-Level Interpretability Analysis of Vision Transformers
by: Zhang, Siyu
Published: (2026)

UnMA-CapSumT: Unified and Multi-Head Attention-driven Caption Summarization Transformer
by: Sharma, Dhruv, et al.
Published: (2024)

Representative Attention For Vision Transformers
by: Li, Yuntong, et al.
Published: (2026)

Vision Transformers with Hierarchical Attention
by: Liu, Yun, et al.
Published: (2021)

IFQA: Interpretable Face Quality Assessment
by: Jo, Byungho, et al.
Published: (2022)

MimiQ: Low-Bit Data-Free Quantization of Vision Transformers with Encouraging Inter-Head Attention Similarity
by: Choi, Kanghyun, et al.
Published: (2024)

Vision Transformers are Circulant Attention Learners
by: Han, Dongchen, et al.
Published: (2025)

Structured Initialization for Attention in Vision Transformers
by: Zheng, Jianqiao, et al.
Published: (2024)

HAViT: Historical Attention Vision Transformer
by: Banik, Swarnendu, et al.
Published: (2026)

Interactive Multi-Head Self-Attention with Linear Complexity
by: Kang, Hankyul, et al.
Published: (2024)

Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching
by: Chen, Wenjing
Published: (2024)

IG-FIQA: Improving Face Image Quality Assessment through Intra-class Variance Guidance robust to Inaccurate Pseudo-Labels
by: Kim, Minsoo, et al.
Published: (2024)

Dynamic Accumulated Attention Map for Interpreting Evolution of Decision-Making in Vision Transformer
by: Liao, Yi, et al.
Published: (2025)

Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
by: Knights, Ethan
Published: (2026)

Polyline Path Masked Attention for Vision Transformer
by: Zhao, Zhongchen, et al.
Published: (2025)

Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
by: Lee, Sanghyeok, et al.
Published: (2024)

Multiscaled Multi-Head Attention-based Video Transformer Network for Hand Gesture Recognition
by: Garg, Mallika, et al.
Published: (2025)

V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
by: Lee, JunGyu, et al.
Published: (2025)

BinaryAttention: One-Bit QK-Attention for Vision and Diffusion Transformers
by: Xiao, Chaodong, et al.
Published: (2026)

SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design
by: Yun, Seokju, et al.
Published: (2024)

Interpretable Vision Transformers in Image Classification via SVDA
by: Arampatzakis, Vasileios, et al.
Published: (2026)

Multi Head Attention Enhanced Inception v3 for Cardiomegaly Detection
by: Karthik, Abishek, et al.
Published: (2025)

MoH: Multi-Head Attention as Mixture-of-Head Attention
by: Jin, Peng, et al.
Published: (2024)

Learning Visual Prompts for Guiding the Attention of Vision Transformers
by: Rezaei, Razieh, et al.
Published: (2024)

Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
by: Gallée, Luisa, et al.
Published: (2025)

B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers
by: Böhle, Moritz, et al.
Published: (2023)

Interpretable Vision Transformers in Monocular Depth Estimation via SVDA
by: Arampatzakis, Vasileios, et al.
Published: (2026)

Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
by: Ma, Chiyu, et al.
Published: (2024)

ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets
by: Kim, Hoyoung, et al.
Published: (2026)

CAMEO: Correspondence-Attention Alignment for Multi-View Diffusion Models
by: Kwon, Minkyung, et al.
Published: (2025)