1 citations · 1 across the 6 of their papers we have counts for
6 papers
Attention Is All You Need For Mixture-of-Depths Routing
Advait Gadhikar, Souptik Kumar Majumdar, Niclas Popp +3
Advancements in deep learning are driven by training models with increasingly larger numbers of parameters, which in turn heightens the computational demands. To address this issue…
Zero-Shot Visual Classification with Guided Cropping
Piyapat Saranrittichai, Mauricio Munoz, Volker Fischer +1
Pretrained vision-language models, such as CLIP, show promising zero-shot performance across a wide variety of datasets. For closed-set classification tasks, however, there is an i…
AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models
Jan Hendrik Metzen, Piyapat Saranrittichai, Chaithanya Kumar Mummadi
Classifiers built upon vision-language models such as CLIP have shown remarkable zero-shot performance across a broad range of image classification tasks. Prior work has studied di…
Multi-Attribute Open Set Recognition
Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta +2
Open Set Recognition (OSR) extends image classification to an open-world setting, by simultaneously classifying known classes and identifying unknown ones. While conventional OSR a…
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain
Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta +2
Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how th…
DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
Elias Eulig, Piyapat Saranrittichai, Chaithanya Kumar Mummadi +4
Common deep neural networks (DNNs) for image classification have been shown to rely on shortcut opportunities (SO) in the form of predictive and easy-to-represent visual factors. T…