3 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception
Wenzhuo Liu, Qiannan Guo, Zhen Wang +9
Advanced Driver Assistance Systems (ADAS) need to understand human driver behavior while perceiving their navigation context, but jointly learning these heterogeneous tasks would c…
AstraNav-World: World Model for Foresight Control and Consistency
Jintao Chen, Junjun Hu, Haochen Bai +11
Embodied navigation in open, dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time. We propose AstraNav-World, an end-t…
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Bowen Zhang +1
Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However…
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, Haichen Zhou +2
Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient dat…