6 citations · 14 across the 26 of their papers we have counts for
28 papers
TSS: Target-Side Sparsification for Speculative Decoding in Domain-Specific Large Language Models
Haibo Hu, Lianming Huang, Qiao Li +2
Speculative decoding accelerates large language model inference through collaboration between a lightweight draft model and a target verifier. Existing methods mainly improve the d…
Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
Haibo HU, Lianming Huang, Qiao Li +2
Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they intr…
Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling
Zikang Zhou, Haibo Hu, Xinhong Chen +5
Multimodal motion forecasting is inherently under-supervised: each training scene provides only one realized future, yet multiple plausible futures exist. This sparse supervision o…
ReFilter: Improving Robustness of Retrieval-Augmented Generation via Gated Filter
Yixin Chen, Ying Xiong, Shangyu Wu +3
Retrieval-augmented generation (RAG) has become a dominant paradigm for grounding large language models (LLMs) with external evidence in knowledge-intensive question answering. A c…
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…
Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems
Ziwei Song, Mingsong Lv, Tianchi Ren +3
Existing Autonomous Driving Systems (ADS) independently make driving decisions, but they face two significant limitations. First, in complex scenarios, ADS may misinterpret the env…
SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion
Jun Wang, Yu Mao, Nan Guan +1
The multimodal model has demonstrated promise in histopathology. However, most multimodal models are based on H\&E and genomics, adopting increasingly complex yet black-box designs…
Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions
Jun Wang, Yu Mao, Nan Guan +1
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analyt…
Retrieval-Augmented Generation for Natural Language Processing: A Survey
Shangyu Wu, Ying Xiong, Yufei Cui +8
Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge. However, LLMs still…
Grade Like a Human: Rethinking Automated Assessment with Large Language Models
Wenjing Xie, Juxin Niu, Chun Jason Xue +1
While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading com…
IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion
Jun Wang, Yu Mao, Yufei Cui +2
Immunohistochemistry (IHC) plays a crucial role in pathology as it detects the over-expression of protein in tissue samples. However, there are still fewer machine learning model s…