79 citations · 174 across the 39 of their papers we have counts for
45 papers
REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling
Devrim Çavuşoğlu, Emre Akbaş
Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models reach 32K tokens only through…
CBX-Bench: A Human-Aligned MLLM Council for Benchmarking Concept Bottleneck Model Explanations
Yusuf Meric Karadag, Gulay Oklan, Seref Baris Cagliyan +2
Concept Bottleneck Models (CBMs) are designed to make visual classification interpretable by expressing predictions through human-understandable concepts. Although interpretability…
Caption Bottleneck Models
Seref Baris Cagliyan, Umut Ozdemir, Merve Tapli +1
Concept Bottleneck Models (CBMs) provide interpretability by routing predictions through a layer of human-understandable concepts. However, defining an optimal concept set for a sp…
Explaining CLIP Zero-shot Predictions Through Concepts
Onat Ozdemir, Anders Christensen, Stephan Alaniz +2
Large-scale vision-language models such as CLIP have achieved remarkable success in zero-shot image recognition, yet their predictions remain largely opaque to human understanding.…
Rethinking Concept Bottleneck Models: From Pitfalls to Solutions
Merve Tapli, Quentin Bouniot, Wolfgang Stammer +2
Concept Bottleneck Models (CBMs) ground predictions in human-understandable concepts but face fundamental limitations: the absence of a metric to pre-evaluate concept relevance, th…
Unsupervised Image Classification with Adaptive Nearest Neighbor Selection and Cluster Ensembles
Melih Baydar, Emre Akbas
Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and…