27 citations · 32 across the 8 of their papers we have counts for
8 papers
Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
Zhenglun Kong, Zheng Zhan, Shiyue Hou +10
Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities…
Prepare the Chair for the Bear! Robot Imagination of Sitting Affordance to Reorient Previously Unseen Chairs
Xin Meng, Hongtao Wu, Sipu Ruan +1
In this letter, a paradigm for the classification and manipulation of previously unseen objects is established and demonstrated through a real example of chairs. We present a novel…
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Zhenglun Kong, Haoyu Ma, Geng Yuan +12
Vision transformers (ViTs) have recently obtained success in many applications, but their intensive computation and heavy memory usage at both training and inference time limit the…
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
Peiyan Dong, Mengshu Sun, Alec Lu +8
While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memo…
Data Level Lottery Ticket Hypothesis for Vision Transformers
Xuan Shen, Zhenglun Kong, Minghai Qin +6
The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the win…
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks
Hongtao Wu, Jikai Ye, Xin Meng +2
Rearrangement tasks have been identified as a crucial challenge for intelligent robotic manipulation, but few methods allow for precise construction of unseen structures. We propos…
SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma +9
Recently, Vision Transformer (ViT) has continuously established new milestones in the computer vision field, while the high computation and memory cost makes its propagation in ind…
Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination
Hongtao Wu, Xin Meng, Sipu Ruan +1
In this paper, we study the problem of autonomously seating a teddy bear on a previously unseen chair. To achieve this goal, we present a novel method for robots to imagine the sit…