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Welcome to TVI Group

We are the Trustworthy Visual Intelligence Group. We focus on pushing the boundaries of Computer Vision, Embodied Intelligence, and Reliable AI.

Trustworthy Visual Intelligence Group

The Trustworthy Visual Intelligence (TVI) group is a core part of the Image Cognition Group at Chongqing University of Posts and Telecommunications. Founded in 2020, we are a dynamic research team led by Associate Professor Jiaxu Leng .

2020 Founded
34 Members
TOP Publications

Our Focus:
Trustworthy AI Video Anomaly Detection Video Analysis Embodied Intelligence

Our members have led projects including the National Natural Science Foundation of China and the Chongqing Bo Xin Plan. We actively publish in top-tier venues:

TPAMI • NeurIPS • ACM CSUR • MM • IEEE TIFS • TMM • TITS • TCSVT • TGRS

Latest News

The team's latest news, events, and announcements will be continuously updated.

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The 3rd Chongqing Brain and Intelligence Academic Seminar

In the wave of deep integration between neuroscience and artificial intelligence, the 3rd Chongqing Brain and Intelligence Academic Symposium was successfully held at the Chongqing Center for Brain and Intelligence Sciences from January 10 to 11, 2026.

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The team 'Sharp Eyes and Quick Hands' won the championship at the 2025 Wuxi International Artificial Intelligence Innovation Application Competition.

On December 12, 2025, the award ceremony for the 3rd Wuxi International Artificial Intelligence Innovation Application Competition was grandly held at the InterContinental Wuxi, and the team won first place.

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Two study on face super-resolution and person re-identification have been accepted by AAAI 2026

The works were jointly supervised by Professor Xinbo Gao and Associate Professor Jiaxu Leng. The two papers focus on cross-modal person re-identification and face super-resolution in AI-computer vision, respectively.

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We proposed 'GARDEN'

The team's latest news, events, and announcements will be continuously updated.

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FEATURED PROJECT

GARDEN: Smart Sanitation Model

Garden adopts a collaborative architecture of 'small perception model + large multimodal model'. It enables precise garbage recognition, road cleanliness evaluation, and intelligent scheduling across multiple scenarios in complex terrains.

INTELLIGENT
SUSTAINABLE