14 papers · 1 filter
Representation Recycling for Streaming Video Analysis
Can Ufuk Ertenli, Ramazan Gokberk Cinbis, Emre Akbas
We present StreamDEQ, a method that aims to infer frame-wise representations on videos with minimal per-frame computation. Conventional deep networks perform feature extraction fro…
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…
RSPose: Ranking Based Losses for Human Pose Estimation
Muhammed Can Keles, Bedrettin Cetinkaya, Sinan Kalkan +1
While heatmap-based human pose estimation methods have shown strong performance, they suffer from three main problems: (P1) "Commonly used Mean Squared Error (MSE)" Loss may not al…