3 accepted papers
Dense visual prediction tasks, including object detection and segmentation, inherently require precise and discriminative positional information to delineate object boundaries and pixel regions. Recent DETR-based frameworks advance dense prediction tasks through iterative attention applied to conten
"This paper presents a general scheme for enhancing the convergence and performance of DETR (DEtection TRansformer). We investigate the slow convergence problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. T…
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention which is proven effective for improving performance but also introduces a…