4 accepted papers
State-of-the-art lane detection methods often rely on specific knowledge about lanes -- such as straight lines and parametric curves -- to detect lane lines. While the specific knowledge can ease the modeling process, it poses challenges in handling lane lines with complex topologies (e.g., dense, f…
Graph-based predictors have recently shown promising results on neural architecture search (NAS). Despite their efficiency, current graph-based predictors treat all operations equally, resulting in biased topological knowledge of cell architectures. Intuitively, not all operations are equally signif…
Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods…
In this paper, we apply self-attention (SA) mechanism to boost the performance of deep metric learning. However, due to the pairwise similarity measurement, the cost of storing and manipulating the complete attention maps makes it infeasible for large inputs. To solve this problem, we propose a com…