6 citations · 14 across the 26 of their papers we have counts for
14 papers · 1 filter
DeeAD: Dynamic Early Exit of Vision-Language Action for Efficient Autonomous Driving
Haibo HU, Lianming Huang, Nan Guan +1
Vision-Language Action (VLA) models unify perception, reasoning, and trajectory generation for autonomous driving, but suffer from significant inference latency due to deep transfo…
On-Demand Multi-Task Sparsity for Efficient Large-Model Deployment on Edge Devices
Lianming Huang, Haibo Hu, Qiao Li +2
Sparsity is essential for deploying large models on resource constrained edge platforms. However, optimizing sparsity patterns for individual tasks in isolation ignores the signifi…
Nav-EE: Navigation-Guided Early Exiting for Efficient Vision-Language Models in Autonomous Driving
Haibo Hu, Lianming Huang, Xinyu Wang +4
Vision-Language Models (VLMs) are increasingly applied in autonomous driving for unified perception and reasoning, but high inference latency hinders real-time deployment. Early-ex…
GM-Skip: Metric-Guided Transformer Block Skipping for Efficient Vision-Language Models
Lianming Huang, Haibo Hu, Qiao Li +3
Transformer-based Vision-Language Models (VLMs) have achieved impressive performance on tasks such as image captioning, object recognition, and visual reasoning, but their high com…
Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Yixin Chen, Ying Xiong, Shangyu Wu +4
Tool-augmented LLMs invoke external functions to extend their capabilities, but errors in the invocation decision, such as calling a tool when none is needed or omitting a needed c…
AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving
Lianming Huang, Haibo Hu, Yufei Cui +4
With the rapid advancement of autonomous driving, deploying Vision-Language Models (VLMs) to enhance perception and decision-making has become increasingly common. However, the rea…
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Yu Mao, Jingzong Li, Jun Wang +4
Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between di…
VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving
Haibo Hu, Jiacheng Zuo, Yang Lou +6
With the widespread adoption and deployment of autonomous driving, handling complex environments has become an unavoidable challenge. Due to the scarcity and diversity of extreme s…
WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression
Yu Mao, Jun Wang, Nan Guan +1
Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides,…
FlexInfer: Breaking Memory Constraint via Flexible and Efficient Offloading for On-Device LLM Inference
Hongchao Du, Shangyu Wu, Arina Kharlamova +2
Large Language Models (LLMs) face challenges for on-device inference due to high memory demands. Traditional methods to reduce memory usage often compromise performance and lack ad…
CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
Tianchi Ren, Haibo Hu, Jiacheng Zuo +5
With the acceleration of urbanization, modern urban traffic systems are becoming increasingly complex, leading to frequent traffic anomalies. These anomalies encompass not only com…
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models
Weilan Wang, Yu Mao, Dongdong Tang +3
Large language models (LLMs) exhibit excellent performance in various tasks. However, the memory requirements of LLMs present a great challenge when deploying on memory-limited dev…
EvoP: Robust LLM Inference via Evolutionary Pruning
Shangyu Wu, Hongchao Du, Ying Xiong +4
Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks, but their massive size and computational demands hinder their deployment in reso…
RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning
Jiacheng Zuo, Haibo Hu, Zikang Zhou +6
In the pursuit of robust autonomous driving systems, models trained on real-world datasets often struggle to adapt to new environments, particularly when confronted with corner cas…