3 citations · 4 across the 11 of their papers we have counts for
12 papers
RoboFL: Federated Expert Assembly for World Action Models
Rongyu Zhang, Ruizhi Fan, Yunfan Lou +8
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heter…
MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation
Ronyu Zhang, Aosong Cheng, Gaole Dai +8
Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture-biased backbones risk error…
SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
Jianing Li, Ming Lu, Hao Wang +4
3D semantic occupancy prediction is a crucial task in visual perception, as it requires the simultaneous comprehension of both scene geometry and semantics. It plays a crucial role…
Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation
Rongyu Zhang, Aosong Cheng, Yulin Luo +8
Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models. As current vision…
T-REX: Mixture-of-Rank-One-Experts with Semantic-aware Intuition for Multi-task Large Language Model Finetuning
Rongyu Zhang, Yijiang Liu, Huanrui Yang +5
Large language models (LLMs) encounter significant adaptation challenges in diverse multitask finetuning. Mixture-of-experts (MoE) provides a promising solution with a dynamic arch…
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis
Jianing Li, Xi Nan, Ming Lu +2
Multi-modal large language models (MLLMs) have demonstrated remarkable vision-language capabilities, primarily due to the exceptional in-context understanding and multi-task learni…
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness
Rongyu Zhang, Zefan Cai, Huanrui Yang +9
Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. However, the conventional finetuning process with randomly sampled data point…
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
Lirui Xiao, Huanrui Yang, Zhen Dong +3
Mixed-precision quantization has been widely applied on deep neural networks (DNNs) as it leads to significantly better efficiency-accuracy tradeoffs compared to uniform quantizati…
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object Detection
Jianing Li, Ming Lu, Jiaming Liu +3
Recently, Bird's-Eye-View (BEV) representation has gained increasing attention in multi-view 3D object detection, which has demonstrated promising applications in autonomous drivin…
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
Yijiang Liu, Huanrui Yang, Zhen Dong +3
The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision tran…
Uncertainty Guided Depth Fusion for Spike Camera
Jianing Li, Jiaming Liu, Xiaobao Wei +6
Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity sce…
Deep Motion Network for Freehand 3D Ultrasound Reconstruction
Mingyuan Luo, Xin Yang, Hongzhang Wang +2
Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky…