{"total":73,"rows":[{"id":"2408.01432","title":"VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance","created":"2024-07-18","cited_by_count":65,"area":null},{"id":"2310.03182","title":"Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models","created":"2023-10-04","cited_by_count":40,"area":null},{"id":"2311.16494","title":"ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models","created":"2023-11-27","cited_by_count":39,"area":"mllm"},{"id":"2503.23283","title":"Language Guided Concept Bottleneck Models for Interpretable Continual Learning","created":"2025-03-30","cited_by_count":28,"area":null},{"id":"2403.07636","title":"Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework","created":"2024-03-12","cited_by_count":27,"area":"mllm"},{"id":"2401.13837","title":"Democratizing Fine-grained Visual Recognition with Large Language Models","created":"2024-01-24","cited_by_count":25,"area":null},{"id":"2312.08924","title":"Training-free Zero-shot Composed Image Retrieval with Local Concept Reranking","created":"2023-12-14","cited_by_count":23,"area":null},{"id":"2408.02265","title":"Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts","created":"2024-08-05","cited_by_count":20,"area":null},{"id":"2404.17753","title":"Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification","created":"2024-04-27","cited_by_count":19,"area":null},{"id":"2404.09941","title":"Evolving Interpretable Visual Classifiers with Large Language Models","created":"2024-04-15","cited_by_count":18,"area":null},{"id":"2402.05945","title":"Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning","created":"2024-02-03","cited_by_count":17,"area":null},{"id":"2404.12652","title":"Pre-trained Vision-Language Models Learn Discoverable Visual Concepts","created":"2024-04-19","cited_by_count":16,"area":"mllm"},{"id":"2410.15364","title":"Scene Graph Generation with Role-Playing Large Language Models","created":"2024-10-20","cited_by_count":16,"area":"mllm"},{"id":"2403.16442","title":"If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions","created":"2024-03-25","cited_by_count":14,"area":"mllm"},{"id":"2312.00110","title":"CLIP-QDA: An Explainable Concept Bottleneck Model","created":"2023-11-30","cited_by_count":13,"area":null},{"id":"2311.11904","title":"LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions","created":"2023-11-20","cited_by_count":11,"area":"mllm"},{"id":"2405.10053","title":"SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection","created":"2024-05-16","cited_by_count":11,"area":null},{"id":"2501.16751","title":"HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging","created":"2025-01-28","cited_by_count":11,"area":null},{"id":"2503.19377","title":"Interpretable Generative Models through Post-hoc Concept Bottlenecks","created":"2025-03-25","cited_by_count":11,"area":null},{"id":"2411.18101","title":"Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis","created":"2024-11-27","cited_by_count":10,"area":null},{"id":"2310.16639","title":"Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Driving","created":"2023-10-25","cited_by_count":9,"area":null},{"id":"2501.04975","title":"V2C-CBM: Building Concept Bottlenecks with Vision-to-Concept Tokenizer","created":"2025-01-09","cited_by_count":9,"area":null},{"id":"2508.14255","title":"Graph Concept Bottleneck Models","created":"2025-08-19","cited_by_count":9,"area":null},{"id":"2404.00701","title":"Training-Free Semantic Segmentation via LLM-Supervision","created":"2024-03-31","cited_by_count":8,"area":null},{"id":"2411.02470","title":"Benchmarking XAI Explanations with Human-Aligned Evaluations","created":"2024-11-04","cited_by_count":8,"area":"alignment"},{"id":"2501.13982","title":"Attribute-based Visual Reprogramming for Vision-Language Models","created":"2025-01-23","cited_by_count":8,"area":"mllm"},{"id":"2403.05297","title":"PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck","created":"2024-03-08","cited_by_count":7,"area":null},{"id":"2406.19130","title":"Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis","created":"2024-06-27","cited_by_count":7,"area":null},{"id":"2407.08947","title":"Constructing Concept-based Models to Mitigate Spurious Correlations with Minimal Human Effort","created":"2024-07-12","cited_by_count":7,"area":null},{"id":"2410.11201","title":"Tree of Attributes Prompt Learning for Vision-Language Models","created":"2024-10-15","cited_by_count":7,"area":"mllm"},{"id":"2412.11917","title":"Does VLM Classification Benefit from LLM Description Semantics?","created":"2024-12-16","cited_by_count":6,"area":null},{"id":"2506.01334","title":"Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models","created":"2025-06-02","cited_by_count":6,"area":"agents"},{"id":"2510.07115","title":"Enhancing Concept Localization in CLIP-based Concept Bottleneck Models","created":"2025-10-08","cited_by_count":6,"area":null},{"id":"2512.10805","title":"Interpretable and Steerable Concept Bottleneck Sparse Autoencoders","created":"2025-12-11","cited_by_count":6,"area":"mechinterp"},{"id":"2509.25717","title":"Importance Sampling for Multi-Negative Multimodal Direct Preference Optimization","created":"2025-09-30","cited_by_count":5,"area":"mllm"},{"id":"2510.04564","title":"Conditional Representation Learning for Customized Tasks","created":"2025-10-06","cited_by_count":5,"area":null},{"id":"2511.22170","title":"Partially Shared Concept Bottleneck Models","created":"2025-11-27","cited_by_count":5,"area":null},{"id":"2405.01825","title":"Improving Concept Alignment in Vision-Language Concept Bottleneck Models","created":"2024-05-03","cited_by_count":4,"area":null},{"id":"2503.20301","title":"Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2508.14562","title":"Locality-aware Concept Bottleneck Model","created":"2025-08-20","cited_by_count":4,"area":null},{"id":"2509.17740","title":"WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification","created":"2025-09-22","cited_by_count":4,"area":"lr"},{"id":"2510.03903","title":"Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models","created":"2025-10-04","cited_by_count":4,"area":"mllm"},{"id":"2603.07343","title":"Learning Concept Bottleneck Models from Mechanistic Explanations","created":"2026-03-07","cited_by_count":4,"area":null},{"id":"2408.13438","title":"Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations","created":"2024-08-24","cited_by_count":3,"area":null},{"id":"2412.13947","title":"Real Classification by Description: Extending CLIP's Limits of Part Attributes Recognition","created":"2024-12-18","cited_by_count":3,"area":"mllm"},{"id":"2603.28211","title":"Explaining CLIP Zero-shot Predictions Through Concepts","created":"2026-03-30","cited_by_count":3,"area":"mllm"},{"id":"2401.03253","title":"VLLaVO: Mitigating Visual Gap through LLMs","created":"2024-01-06","cited_by_count":2,"area":"mllm"},{"id":"2410.10663","title":"Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2411.18651","title":"Verbalized Representation Learning for Interpretable Few-Shot Generalization","created":"2024-11-27","cited_by_count":2,"area":null},{"id":"2411.19757","title":"Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models","created":"2024-11-29","cited_by_count":2,"area":null},{"id":"2501.06031","title":"Generate, Transduct, Adapt: Iterative Transduction with VLMs","created":"2025-01-10","cited_by_count":2,"area":"mllm"},{"id":"2501.13341","title":"Multi-aspect Knowledge Distillation with Large Language Model","created":"2025-01-23","cited_by_count":2,"area":"distill"},{"id":"2502.16733","title":"Coreset Selection via LLM-based Concept Bottlenecks","created":"2025-02-23","cited_by_count":2,"area":null},{"id":"2504.00185","title":"Self-Evolving Visual Concept Library using Vision-Language Critics","created":"2025-03-31","cited_by_count":2,"area":"synth"},{"id":"2507.08410","title":"Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models","created":"2025-07-11","cited_by_count":2,"area":"mllm"},{"id":"2510.24105","title":"Enhancing Pre-trained Representation Classifiability can Boost its Interpretability","created":"2025-10-28","cited_by_count":2,"area":null},{"id":"2603.05629","title":"Rethinking Concept Bottleneck Models: From Pitfalls to Solutions","created":"2026-03-05","cited_by_count":2,"area":"lr"},{"id":"2311.10933","title":"Representing visual classification as a linear combination of words","created":"2023-11-18","cited_by_count":1,"area":null},{"id":"2410.13651","title":"Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2412.00364","title":"LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation","created":"2024-11-30","cited_by_count":1,"area":null},{"id":"2502.20393","title":"Walking the Web of Concept-Class Relationships in Incrementally Trained Interpretable Models","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2606.00082","title":"Hoeffding Concept Bottleneck Models with Applications to Overhead Images","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2607.00578","title":"Caption Bottleneck Models","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2311.09680","title":"Trustworthy Large Models in Vision: A Survey","created":"2023-11-16","cited_by_count":0,"area":null},{"id":"2401.16347","title":"Cross-Modal Coordination Across a Diverse Set of Input Modalities","created":"2024-01-29","cited_by_count":0,"area":null},{"id":"2406.08840","title":"Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2507.03542","title":"Beyond Accuracy: Metrics that Uncover What Makes a 'Good' Visual Descriptor","created":"2025-07-04","cited_by_count":0,"area":"mllm"},{"id":"2509.17522","title":"Chat-CBM: Towards Interactive Concept Bottleneck Models with Frozen Large Language Models","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2512.15748","title":"Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors","created":"2025-12-10","cited_by_count":0,"area":"mllm"},{"id":"2603.08921","title":"Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning","created":"2026-03-09","cited_by_count":0,"area":"mllm"},{"id":"2604.11986","title":"Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2606.19489","title":"Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks","created":"2026-06-17","cited_by_count":0,"area":"lr"},{"id":"2606.19882","title":"Multimodal Concept Bottleneck Models","created":"2026-06-18","cited_by_count":0,"area":null}],"next_cursor":null}