1 citations · 1 across the 4 of their papers we have counts for
4 papers
Do Transformers Understand Ancient Roman Coin Motifs Better than CNNs?
David Reid, Ognjen Arandjelovic
Automated analysis of ancient coins has the potential to help researchers extract more historical insights from large collections of coins and to help collectors understand what th…
Would I Lie To You? Inference Time Alignment of Language Models using Direct Preference Heads
Avelina Asada Hadji-Kyriacou, Ognjen Arandjelovic
Pre-trained Language Models (LMs) exhibit strong zero-shot and in-context learning capabilities; however, their behaviors are often difficult to control. By utilizing Reinforcement…
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning
Avelina Asada Hadji-Kyriacou, Ognjen Arandjelovic
This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models. PEFT techniques such…
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes
Yifei Qian, Xiaopeng Hong, Zhongliang Guo +2
To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data,…