activity
20192022
most citediffDetector: Inference-aware Feature Filtering for Object Detection

5 citations · 17 across the 8 of their papers we have counts for

collaborators

11 papers

cs.DC20222 cited

DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg

Yuhao Zhou, Minjia Shi, Yuxin Tian +2

Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing…

cs.CV20225 cited

Image Search with Text Feedback by Additive Attention Compositional Learning

Yuxin Tian, Shawn Newsam, Kofi Boakye

Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desi…

cs.RO2021

Hierarchical Segment-based Optimization for SLAM

Yuxin Tian, Yujie Wang, Ming Ouyang +1

This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation me…

cs.CV2021

Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations

Zike Yan, Yuxin Tian, Xuesong Shi +3

Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene propertie…

cs.CV20213 cited

AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation

Xueqing Deng, Yi Zhu, Yuxin Tian +1

Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case. However, such data i…

cs.CV20211 cited

Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments

Yuxin Tian, Jinyu MIao, Xingming Wu +3

Features play an important role in various visual tasks, especially in visual place recognition applied in perceptual changing environments. In this paper, we address the challenge…