3 papers
cs.CL2026
Pretraining with hierarchical memories: separating long-tail and common knowledge
Hadi Pouransari, David Grangier, C Thomas +2
The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all wor…
cs.CV2025
LangDA: Building Context-Awareness via Language for Domain Adaptive Semantic Segmentation
Chang Liu, Bavesh Balaji, Saad Hossain +5
Unsupervised domain adaptation for semantic segmentation (DASS) aims to transfer knowledge from a label-rich source domain to a target domain with no labels. Two key approaches in…
cs.CV2024
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment
Alvi Md Ishmam, Christopher Thomas
In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However, the use of large-scale datasets scraped from the web f…