Imagen de Portada

DCBM: Data-Efficient Visual Concept Bottleneck Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Prasse, Katharina, Knab, Patrick, Marton, Sascha, Bartelt, Christian, Keuper, Margret
Formato:	Preprint
Publicado:	2024
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification
por: Knab, Patrick, et al.
Publicado: (2025)

From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media
por: Prasse, Katharina, et al.
Publicado: (2026)

Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
por: Knab, Patrick, et al.
Publicado: (2024)

I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset Frames
por: Prasse, Katharina, et al.
Publicado: (2024)

Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition
por: Prasse, Katharina, et al.
Publicado: (2023)

ClimateVID -- Social Media Videos Analysis and Challenges Involved
por: Xu, Shiqi, et al.
Publicado: (2026)

Deepfakes: we need to re-think the concept of "real" images
por: Keuper, Janis, et al.
Publicado: (2025)

Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models
por: Müller, Patrick, et al.
Publicado: (2025)

As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain
por: Grabinski, Julia, et al.
Publicado: (2023)

Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
por: Lorenz, Peter, et al.
Publicado: (2022)

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models
por: Fatima, Mishal, et al.
Publicado: (2025)

Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X
por: Prasse, Katharina, et al.
Publicado: (2025)

Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Images
por: Walter, Jan Philip, et al.
Publicado: (2026)

Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition
por: Xu, Jiawen, et al.
Publicado: (2024)

CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
por: Agnihotri, Shashank, et al.
Publicado: (2023)

Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context
por: Agnihotri, Shashank, et al.
Publicado: (2023)

Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
por: Lorenz, Peter, et al.
Publicado: (2021)

On the Concept Trustworthiness in Concept Bottleneck Models
por: Huang, Qihan, et al.
Publicado: (2024)

How Do Training Methods Influence the Utilization of Vision Models?
por: Gavrikov, Paul, et al.
Publicado: (2024)

BARISTA: A Multi-Task Egocentric Benchmark for Compositional Visual Understanding
por: Knab, Patrick, et al.
Publicado: (2026)

Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail
por: Lamm, Bianca, et al.
Publicado: (2024)

FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
por: Medi, Tejaswini, et al.
Publicado: (2024)

Smart Eyes for Silent Threats: VLMs and In-Context Learning for THz Imaging
por: Poggi, Nicolas, et al.
Publicado: (2025)

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models
por: Xie, Yan, et al.
Publicado: (2025)

Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling
por: Grabinski, Julia, et al.
Publicado: (2023)

Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations
por: Gong, Shizhan, et al.
Publicado: (2026)

Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
por: Zhang, Haifei, et al.
Publicado: (2025)

Partially Shared Concept Bottleneck Models
por: Zhao, Delong, et al.
Publicado: (2025)

Locality-aware Concept Bottleneck Model
por: Jeon, Sujin, et al.
Publicado: (2025)

Detecting AutoAttack Perturbations in the Frequency Domain
por: Lorenz, Peter, et al.
Publicado: (2021)

AIM: Amending Inherent Interpretability via Self-Supervised Masking
por: Alshami, Eyad, et al.
Publicado: (2025)

Improving Concept Alignment in Vision-Language Concept Bottleneck Models
por: Selvaraj, Nithish Muthuchamy, et al.
Publicado: (2024)

Concept-wise Attention for Fine-grained Concept Bottleneck Models
por: Zhong, Minghong, et al.
Publicado: (2026)

Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
por: Kazmierczak, Rémi, et al.
Publicado: (2025)

Towards Class-wise Robustness Analysis
por: Medi, Tejaswini, et al.
Publicado: (2024)

TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
por: Medi, Tejaswini, et al.
Publicado: (2025)

Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks
por: Kleinmann, Marcel, et al.
Publicado: (2025)

Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation
por: Xu, Jiawen, et al.
Publicado: (2025)

Explainable Visual Anomaly Detection via Concept Bottleneck Models
por: Stropeni, Arianna, et al.
Publicado: (2025)

A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification
por: Lamm, Bianca, et al.
Publicado: (2025)