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Jiaxi Wu

7 accepted papers

2023

COVID-19 Vaccine Misinformation in Middle Income Countries

EMNLP 2023long main

This paper introduces a multilingual dataset of COVID-19 vaccine misinformation, consisting of annotated tweets from three middle-income countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset includes annotations for 5,952 tweets, assessing their relevance to COVID-19 vaccines, pres…

Cited by 0SourcecodeScholar
2023

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

ICRA 2023poster

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat…

Cited by 6SourceScholar
2022

Cross Domain Object Detection by Target-Perceived Dual Branch Distillation

CVPR 2022poster

Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly focus on either of these two difficulties, even thou…

Cited by 90PDFcodeScholar
2022

Entropy-Based Active Learning for Object Detection With Progressive Diversity Constraint

CVPR 2022poster

Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label. Active learning for object detection is more challenging and existing efforts on it are relatively rare. In this paper, w…

Cited by 81PDFcodeScholar
2022

Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

CVPR 2022oral

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during…

Cited by 31PDFScholar
2020

Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

ECCV 2020poster

Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highligh…