Search Results for author:
Found 4 papers, 1 papers with code
Gen2Det: Generate to Detect
In the long-tailed detection setting on LVIS, Gen2Det improves the performance on rare categories by a large margin while also significantly improving the performance on other categories, e. g. we see an improvement of 2. 13 Box AP and 1. 84 Mask AP over just training on real data on LVIS with Mask R-CNN.
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
To bootstrap the research on EgoObjects, we present a suite of 4 benchmark tasks around the egocentric object understanding, including a novel instance level- and the classical category level object detection.
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Although acquired extensive knowledge of visual concepts, it is non-trivial to exploit knowledge from these VL models to the task of semantic segmentation, as they are usually trained at an image level.
Exploring Open-Vocabulary Semantic Segmentation from CLIP Vision Encoder Distillation Only
Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level.
