Search Results for author:
Found 14 papers, 5 papers with code
DREAM-PCD: Deep Reconstruction and Enhancement of mmWave Radar Pointcloud
However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and strong interference and noise.
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms
In this paper, we hypothesize that snippets with similar representations should be considered as the same action class despite the absence of supervision signals on each snippet.
Multiple Instance Learning
Weakly Supervised Action Localization
+2
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge
And in order to make the most of visual inductive priors, we designed a task-specific inference processing strategy.
Strong Instance Segmentation Pipeline for MMSports Challenge
The goal of ACM MMSports2022 DeepSportRadar Instance Segmentation Challenge is to tackle the segmentation of individual humans including players, coaches and referees on a basketball court.
Style Variable and Irrelevant Learning for Generalizable Person Re-identification
In this paper, we first verify through an experiment that style factors are a vital part of domain bias.
Domain Generalization
Generalizable Person Re-identification
+1
Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval
Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description.
Unsupervised Domain Adaptation for Device-free Gesture Recognition
To tackle this challenge, we propose an unsupervised domain adaptation framework for device free gesture recognition by making effective use of the unlabeled target domain data.
Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal
To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations.
Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
Vehicle Re-identification aims to identify a specific vehicle across time and camera view.
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method.
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
Shapley Interpretation and Activation in Neural Networks
Our method is based on an accurate analytical approximation to the Shapley value of a neuron with ReLU activation.
Reduced Form Capital Optimization
We formulate banks' capital optimization problem as a classic mean variance optimization, by leveraging an accurate linear approximation to the Shapely or Constrained Aumann-Shapley (CAS) allocation of max or nested max cost functions.
