43 citations · 49 across the 7 of their papers we have counts for
6 papers · 1 filter
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31
Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…
Flexible visual prompts for in-context learning in computer vision
Thomas Foster, Ioana Croitoru, Robert Dorfman +3
In this work, we address in-context learning (ICL) for the task of image segmentation, introducing a novel approach that adapts a modern Video Object Segmentation (VOS) technique f…
Cross Modal Retrieval with Querybank Normalisation
Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin +2
Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-…
TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu +4
In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerfu…
Unsupervised learning of foreground object detection
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Unsupervised learning poses one of the most difficult challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence…
Unsupervised learning from video to detect foreground objects in single images
Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Unsupervised learning from visual data is one of the most difficult challenges in computer vision, being a fundamental task for understanding how visual recognition works. From a p…