Surface reconstruction from noisy, non-uniformly, and unoriented point clouds is a fascinating yet difficult problem in computer vision and computer graphics.
In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501.
With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency.
Ranked #1 on Scene Text Detection on ICDAR 2015 (Accuracy metric)
This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL).
However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states.
1 code implementation • 3 Nov 2020 • Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
Specifically, LAFE utilizes the region attention modules and channel attention modules to extract discriminative features and confusable features respectively.
First, we introduce a batch coherence-guided channel attention (BCCA) module that highlights the relevant channels for each respective part from the output of a deep backbone model.
The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations.
The feature decomposition network decomposes feature embedding into independent and correlated parts such that the correlations between features will be highlighted.
MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain.
Ranked #4 on Person Re-Identification on CUHK03 detected
Detecting scene text of arbitrary shapes has been a challenging task over the past years.
Ranked #24 on Scene Text Detection on ICDAR 2015
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
This report summarises our method and validation results for the ISIC Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1: Lesion Segmentation.
Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.
In this work, we propose to reason over knowledge base embeddings for personalized recommendation.