IEEE ICIP

Size-Informed Representations for Unsupervised Image Clustering
This paper investigates how image size and preprocessing impact unsupervised clustering, introducing a late-fusion size injection strategy that significantly improves performance on heterogeneous real-world datasets.
Philipp Rajah Moura Srivastava, Charilaos Apostolidis, Saumya Pailwan, Patrick Koller, Leandros Stefanopoulos
Cite DOI IEEE Xplore
Size-Informed Representations for Unsupervised Image Clustering
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
This paper introduces a novel ’network surgery’ approach that integrates CNNs with CLIP to provide caption-based explanations. By moving beyond potentially misleading saliency maps, this method identifies the dominant semantic concepts driving a model’s prediction, enabling better detection of spurious biases and improving overall model robustness.
Patrick Koller, Amil v. Dravid, Prof. Dr. Guido Schuster, Prof. Dr. Aggelos Katsaggelos
Cite Code Poster DOI IEEE Xplore arXiv
Caption-Driven Explainability: Probing CNNs for Bias via CLIP