Hang Li

I am a PhD student advised by Prof. Volker Tresp at Ludwig Maximillian University of Munich (LMU) and Siemens AG. I work on cognitive deep learning, knowledge graphs, and computer vision.

I acquired my master's degree in Robotics, Cognition, Intelligence at the department of computer science at Technical University of Munich. My master's thesis is about scene graph reasoning for visual question answering, supervised by Prof. Stephan Günnemann and Prof. Volker Tresp.

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Research

My research focuses on a wide range of deep learning topics, particularly in cognitive deep learning, multimodal knowledge graphs, representation learning, and visual question answering. I'm interested in the intersection of cognitive neuroscience and artificial intelligence.

The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Volker Tresp, Sahand Sharifzadeh*, Hang Li*, Dario Konopatzki, Yunpu Ma
preprint, 2021
code / arXiv

We present a unified computational theory of an agent’s perception and memory. Episodic memory and semantic memory evolved as emergent properties in a development to gain a deeper understanding of sensory information, to provide a context, and to provide a sense of the current state of the world.

Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering
Rajat Koner*, Hang Li*, Marcel Hildebrandt*, Deepan Das, Volker Tresp, Stephan Günnemann
ISWC, 2021
code / arXiv

We find that Graphhopper outperforms state-of-the-art scene graph reasoning model on both manually curated and automatically generated scene graphs by a significant margin.

Scene Graph Reasoning for Visual Question Answering
Marcel Hildebrandt*, Hang Li*, Rajat Koner*, Volker Tresp, Stephan Günnemann
ICML Workshop, 2020
code / arXiv

We propose a novel method that approaches the VQA task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.


Last updated: 03 Nov 2021
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