Yiming Wang
My research enthusiasm is on vision-based scene understanding that facilitates robotic automation for social good, covering diverse topics on static scene modelling, semantic scene understanding and scene dynamic analysis.
I obtained the PhD in Electronic Engineering in 2018 from Queen Mary University of London (UK) under the supervision of Prof. Andrea Cavallaro, working on vision-based multi-agent navigation. Since 2018, I worked as a post-doc researcher in the Pattern Analysis and Computer Vision/Visual Geometry Modelling (PAVIS/VGM) research line led by Dr. Alessio Del Bue at Istituto Italiano di Tecnologia (IIT), working on topics related to active 3D vision through both research and industrial Projects. Currently I work as a researcher in the Deep Visual Learning (DVL) unit led by Prof. Elisa Ricci in Fondazione Bruno Kessler (FBK), working on privacy-preserving scene dynamics analysis in the context of Smart City under a couple of EU Projects.
News
Tweets
21-09-2023: 🎉 Our amazing paper "Vocabulary-free Image Classification" is accepted by NeurIPS!
16-08-2023: 🎉 Glad to announce that our survey paper on "Video anomaly detection in dymanic scenes with moving cameras" is published on AI Reviews!
31-07-2023: 💚 Happy to receive my first grant in career and look forward to making a green impact with LoCa AI.
26-04-2023: 🎉 Proud to have our paper "Leveraging commonsense for object localisation in partial scenes" accepted in TPAMI!
15-03-2023: 🤖 Glad to announce the CfP to our Special Issue "Social and Cognitive Interactions in the Open World" on International Journal of Social Robotics!
17-02-2023: 🎉 Our paper "PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation" is accepted in ICASSP 2023!
17-01-2023: 🎉 Our paper "3DSGrasp: 3D Shape-Completion for Robotic Grasp" is accepted in ICRA 2023!
11-10-2022: 🎉 Our paper "ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing" is accepted in WACV 2023!
01-10-2022: 🎉 Our paper "Fast re-OBJ: Real-time object re-identification in rigid scenes" is finally accepted in Machine Vision and Applications!
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