Search Results for author:
Found 7 papers, 3 papers with code
ProIn: Learning to Predict Trajectory Based on Progressive Interactions for Autonomous Driving
In this work, a progressive interaction network is proposed to enable the agent's feature to progressively focus on relevant maps, in order to better learn agents' feature representation capturing the relevant map constraints.
RMT: Retentive Networks Meet Vision Transformers
To alleviate these issues, we draw inspiration from the recent Retentive Network (RetNet) in the field of NLP, and propose RMT, a strong vision backbone with explicit spatial prior for general purposes.
3D Small Object Detection with Dynamic Spatial Pruning
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.
Building Vision Transformers with Hierarchy Aware Feature Aggregation
Thanks to the excellent global modeling capability of attention mechanisms, the Vision Transformer has achieved better results than ConvNet in many computer tasks.
Efficient Style-Corpus Constrained Learning for Photorealistic Style Transfer
Photorealistic style transfer is a challenging task, which demands the stylized image remains real.
Progressive Multi-Stage Learning for Discriminative Tracking
Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation.
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
(2) It is difficult to obtain users' explicit feedback of their preference in product features.
