Cover Image

ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sinha, Sanchit, Xiong, Guangzhi, Zhang, Aidong
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision Language Models
by: Sinha, Sanchit, et al.
Published: (2025)

CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
by: He, Zhenghao, et al.
Published: (2026)

GCAV: A Global Concept Activation Vector Framework for Cross-Layer Consistency in Interpretability
by: He, Zhenghao, et al.
Published: (2025)

Concept-RuleNet: Grounded Multi-Agent Neurosymbolic Reasoning in Vision Language Models
by: Sinha, Sanchit, et al.
Published: (2025)

Structural Causality-based Generalizable Concept Discovery Models
by: Sinha, Sanchit, et al.
Published: (2024)

A Self-explaining Neural Architecture for Generalizable Concept Learning
by: Sinha, Sanchit, et al.
Published: (2024)

CoLiDR: Concept Learning using Aggregated Disentangled Representations
by: Sinha, Sanchit, et al.
Published: (2024)

Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
by: Sinha, Sanchit, et al.
Published: (2026)

Retrieving Counterfactuals Improves Visual In-Context Learning
by: Xiong, Guangzhi, et al.
Published: (2026)

Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
by: Sinha, Sanchit, et al.
Published: (2025)

Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
by: Xiong, Guangzhi, et al.
Published: (2026)

STRAP-ViT: Segregated Tokens with Randomized -- Transformations for Defense against Adversarial Patches in ViTs
by: Chattopadhyay, Nandish, et al.
Published: (2026)

LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons
by: Nag, Shashank, et al.
Published: (2025)

ViTCAE: ViT-based Class-conditioned Autoencoder
by: Jebraeeli, Vahid, et al.
Published: (2025)

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
by: Xiong, Guangzhi, et al.
Published: (2024)

Octic Vision Transformers: Quicker ViTs Through Equivariance
by: Nordström, David, et al.
Published: (2025)

TAP-ViTs: Task-Adaptive Pruning for On-Device Deployment of Vision Transformers
by: Wang, Zhibo, et al.
Published: (2026)

Communication Efficient Split Learning of ViTs with Attention-based Double Compression
by: Alvetreti, Federico, et al.
Published: (2025)

ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
by: Cho, Ikhyun, et al.
Published: (2024)

S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks
by: S, Neha A, et al.
Published: (2024)

Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity
by: Xiong, Guangzhi, et al.
Published: (2026)

Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers
by: Li, Zhengang, et al.
Published: (2024)

ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images
by: Bourriez, Nicolas, et al.
Published: (2023)

SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers
by: Venkatraman, Shravan, et al.
Published: (2024)

Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
by: Kang, Ben, et al.
Published: (2025)

LetheViT: Selective Machine Unlearning for Vision Transformers via Attention-Guided Contrastive Learning
by: Tong, Yujia, et al.
Published: (2025)

ZACH-ViT: A Zero-Token Vision Transformer with ShuffleStrides Data Augmentation for Robust Lung Ultrasound Classification
by: Angelakis, Athanasios, et al.
Published: (2025)

MuViT: Multi-Resolution Vision Transformers for Learning Across Scales in Microscopy
by: Mantes, Albert Dominguez, et al.
Published: (2026)

A Comprehensive Survey on the Risks and Limitations of Concept-based Models
by: Sinha, Sanchit, et al.
Published: (2025)

How to train your ViT for OOD Detection
by: Mueller, Maximilian, et al.
Published: (2024)

Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey
by: Yunusa, Haruna, et al.
Published: (2024)

ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification
by: Saraei, Mohammadreza, et al.
Published: (2025)

HEAL-ViT: Vision Transformers on a spherical mesh for medium-range weather forecasting
by: Ramavajjala, Vivek
Published: (2024)

TPC-ViT: Token Propagation Controller for Efficient Vision Transformer
by: Zhu, Wentao
Published: (2024)

HydraViT: Stacking Heads for a Scalable ViT
by: Haberer, Janek, et al.
Published: (2024)

FasterViT: Fast Vision Transformers with Hierarchical Attention
by: Hatamizadeh, Ali, et al.
Published: (2023)

ViT-AdaLA: Adapting Vision Transformers with Linear Attention
by: Li, Yifan, et al.
Published: (2026)

DAVE: Distribution-aware Attribution via ViT Gradient Decomposition
by: Wróbel, Adam, et al.
Published: (2026)

Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
by: Chen, Lu, et al.
Published: (2025)

ViT Enhanced Privacy-Preserving Secure Medical Data Sharing and Classification
by: Amin, Al, et al.
Published: (2024)