Explainable Image Classification

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Zero-Shot Faithful Textual Explanations via Directional-Derivative Influence on Predictions
May 16, 2026Toshinori Yamauchi, Hiroshi Kera, Kazuhiko KawamotoGradient-Based Attribution Neural Network Interpretability
Chiba University · National Institute of Informatics
Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
May 13, 2026Nicola Debole, Andrea Passerini, Stefano Teso +2Concept Bottleneck Models Human-in-the-Loop Annotation
DISI, University of Trento, Italy · CIMeC, University of Trento, Italy
AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers
May 12, 2026Raj Kiran Gupta KatakamGradient-Based Attribution Feature Attribution
Credit Karma, San Francisco, CA, USA
FAME: Feature Activation Map Explanation on Image Classification and Face Recognition
May 12, 2026Xinyi Zhang, Manuel GüntherGradient-Based Attribution Feature Attribution
Department of Informatics, University of Zurich
Explainable Part-Based Vehicle Classifier with Spatial Awareness
May 8, 2026Andreas Caduff, Klaus Zahn, Jonas Hofstetter +2Intelligent Transportation Systems Explainable Image Classification
Competence Center for Intelligent Sensors and Networks, Lucerne University of Applied Science and Art · Competence Center for Intelligent Sensors and Networks, Lucerne University of Applied Science and Art, Technikumstr. 21, Horw, 6048, Switzerland. · SICK AG +1
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
May 7, 2026Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +1Abductive Reasoning Concept-Based Explanations
Colorado State University, Fort Collins CO, USA · KBR Inc., NASA Ames, Moffett Field CA, USA · Carnegie Mellon University, Pittsburgh PA, USA
Automated Palynological Analysis System: Integrating Deep Metric Learning, Detection and Classification in Bright Field Microscopy
Apr 17, 2026J. Staforelli-Vivanco, R. Jofré, P. Coelho +12Salient Object Detection Explainable Image Classification
Facultad de Ingeniería, Universidad Andres Bello, Talcahuano, CCP-THNO 7100 Chile · Universidad Arturo Prat, Sede Victoria, Chile · Laboratory of Palynology and Plant Ecology, School of Sciences and Technologies, University of Concepción, Los Angeles Campus
Prototype-Grounded Concept Models for Verifiable Concept Alignment
Apr 17, 2026Stefano Colamonaco, David Debot, Pietro Barbiero +1Concept Bottleneck Models Interpretable ML
Department of Computer Science, KU Leuven · IBM Research, Zurich
VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts
Mar 13, 2025Chuqin Geng, Yuhe Jiang, Li Zhang +5Explainable Artificial Intelligence Concept-Based Explanations
School of Computer Science, McGill University, Montreal, Canada · Department of Computer Science, University of Toronto, Toronto, Canada
Causal Explanations for Image Classifiers
Nov 13, 2024Hana Chockler, David A. Kelly, Daniel Kroening +1Actual Causality Explainable Image Classification
King’s College London, London, UK · Amazon.com, Inc.,, USA · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE +1

May 16, 2026, cs.CVJ/K move · Enter open · S save

Zero-Shot Faithful Textual Explanations via Directional-Derivative Influence on Predictions

Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto

Chiba University · National Institute of Informatics

Gradient-Based Attribution Neural Network Interpretability Explainable Image Classification Explainability Evaluation

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Zero-shot textual explanations aim to make image classifiers more transparent by probing their internal representations, without relying on task-specific supervision or LVLMs. However, existing methods often miss the features that truly drive the prediction, resulting in limited \textit{faithfulness} to the evidence underlying the model's decision. To address this, we propose FaithTrace. Motivated by the idea that faithful explanations should describe concepts that strongly influence the prediction, FaithTrace directly measures how much the representation induced by the explanation changes the class logit. We introduce an influence score, computed as the directional derivative of the class logit along the text-induced direction in the classifier's feature space, and use it as a proxy for faithfulness. Moreover, we extend this influence score into quantitative evaluation metrics, helping fill the gap in faithfulness evaluation for textual explanations. Experiments show that FaithTrace yields more faithful explanations than baselines, facilitating a more accurate understanding of the model. The code will be publicly released.

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