Cover Image

Interpreting Neurons in Deep Vision Networks with Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bai, Nicholas, Iyer, Rahul A., Oikarinen, Tuomas, Kulkarni, Akshay, Weng, Tsui-Wei
Format:	Preprint
Published:	2024
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

Linear Explanations for Individual Neurons
by: Oikarinen, Tuomas, et al.
Published: (2024)

Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability
by: Oikarinen, Tuomas, et al.
Published: (2025)

Interpretable Generative Models through Post-hoc Concept Bottlenecks
by: Kulkarni, Akshay, et al.
Published: (2025)

CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
by: Javadi, Amirhosein, et al.
Published: (2026)

Interpretability-Guided Test-Time Adversarial Defense
by: Kulkarni, Akshay, et al.
Published: (2024)

Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
by: Kulkarni, Akshay, et al.
Published: (2025)

VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance
by: Srivastava, Divyansh, et al.
Published: (2024)

Crafting Large Language Models for Enhanced Interpretability
by: Sun, Chung-En, et al.
Published: (2024)

Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
by: Yan, Ge, et al.
Published: (2025)

Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
by: Oikarinen, Tuomas, et al.
Published: (2025)

Medical Vision Language Models as Policies for Robotic Surgery
by: Muppidi, Akshay, et al.
Published: (2025)

RAT: Boosting Misclassification Detection Ability without Extra Data
by: Yan, Ge, et al.
Published: (2025)

Concept Bottleneck Large Language Models
by: Sun, Chung-En, et al.
Published: (2024)

LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
by: Flora, James, et al.
Published: (2026)

Provably Robust Conformal Prediction with Improved Efficiency
by: Yan, Ge, et al.
Published: (2024)

Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
by: Qi, Shuren, et al.
Published: (2024)

Towards Interpreting Visual Information Processing in Vision-Language Models
by: Neo, Clement, et al.
Published: (2024)

Cluster Paths: Navigating Interpretability in Neural Networks
by: Kroeger, Nicholas M., et al.
Published: (2025)

Improved Object-Based Style Transfer with Single Deep Network
by: Kulkarni, Harshmohan, et al.
Published: (2024)

IPO: Interpretable Prompt Optimization for Vision-Language Models
by: Du, Yingjun, et al.
Published: (2024)

On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
by: Plebe, Alice, et al.
Published: (2020)

Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations
by: Jiang, Nick, et al.
Published: (2024)

MI CAM: Mutual Information Weighted Activation Mapping for Causal Visual Explanations of Convolutional Neural Networks
by: Iyer, Ram S, et al.
Published: (2025)

Discretized Quadratic Integrate-and-Fire Neuron Model for Deep Spiking Neural Networks
by: Jahns, Eric, et al.
Published: (2025)

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization
by: Meng, Xiang, et al.
Published: (2024)

CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models
by: Wang, Qinsi, et al.
Published: (2025)

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models
by: Zhang, Mingyuan, et al.
Published: (2025)

Neutral-Reference Prompting for Vision-Language Models
by: Tian, Senmao, et al.
Published: (2026)

Perturbation on Feature Coalition: Towards Interpretable Deep Neural Networks
by: Hu, Xuran, et al.
Published: (2024)

A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation
by: Zhang, Yabin, et al.
Published: (2026)

Assessing Visual Privacy Risks in Multimodal AI: A Novel Taxonomy-Grounded Evaluation of Vision-Language Models
by: Tsaprazlis, Efthymios, et al.
Published: (2025)

EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
by: Vukadinovic, Milos, et al.
Published: (2024)

FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing
by: Corley, Isaac, et al.
Published: (2025)

Patronus: Interpretable Diffusion Models with Prototypes
by: Weng, Nina, et al.
Published: (2025)

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task
by: Maleki, Alireza, et al.
Published: (2024)

Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
by: Wang, Bin, et al.
Published: (2022)

Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
by: Xing, Shuo, et al.
Published: (2025)

Learning without Forgetting for Vision-Language Models
by: Zhou, Da-Wei, et al.
Published: (2023)

VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
by: Shen, Shufan, et al.
Published: (2025)

Interpretable Diffusion Models with B-cos Networks
by: Bernold, Nicola, et al.
Published: (2025)