7 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation
Chandler Timm C. Doloriel, Yunbei Zhang, Sarthak Kumar Maharana +5
Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment is fragile under continual test-…
Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting
Chandler Timm C. Doloriel, Yunbei Zhang, Muhammad Salman Siddiqui +4
Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA with batch size one and no acce…
CAST: Closed-form Analytic Semantic Transfer for Zero-Shot Classifier Extension
William Heyden, Habib Ullah, Muhammad Salman Siddiqui +1
Large pre-trained models have become foundational components of modern machine learning systems. Yet adapting these models to novel categories typically requires examples from the…
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning
William Heyden, Habib Ullah, M. Salman Siddiqui +1
In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates int…
SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning
William Heyden, Habib Ullah, M. Salman Siddiqui +1
Zero-Shot Learning (ZSL) presents the challenge of identifying categories not seen during training. This task is crucial in domains where it is costly, prohibited, or simply not fe…
An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning
William Heyden, Habib Ullah, M. Salman Siddiqui +1
Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semanti…