10 citations · 54 across the 33 of their papers we have counts for
35 papers
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models
Guoming Ling, Yupei Lin, Zhongzhan Huang +5
Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchm…
Agent-driven Long-tail Simulation for Autonomous Driving
Junru Gu, Lijin Yang, Jianing Huang +3
Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on log replay or rule-based agents…
Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning
Cong Wan, Ying He, Zhongzhan Huang +1
Test-time Scaling (TTS) has emerged as a pivotal research direction for enhancing model performance by dynamically allocating computational resources during inference. Recent advan…
BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving
George Ling, Lijin Yang, Hao Yang +1
We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis, we reveal that language ma…
Judge, Then Drive: A Critic-Centric Vision Language Action Framework for Autonomous Driving
Lijin Yang, Jianing Huang, Zhongzhan Huang +2
Recent advances in vision language action (VLA) models have shown remarkable potential for autonomous driving by directly mapping multimodal inputs to control signals. However, pre…
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
Guoming Ling, Zhongzhan Huang, Yupei Lin +4
Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially…
Multimodal Representation-disentangled Information Bottleneck for Multimodal Recommendation
Hui Wang, Jinghui Qin, Wushao Wen +3
Multimodal data has significantly advanced recommendation systems by integrating diverse information sources to model user preferences and item characteristics. However, these syst…
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Yifan Liu, Wenkuan Zhao, Shanshan Zhong +4
Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Amon…
BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
Shu Liu, Wenlin Chen, Weihao Li +7
Diffusion-based planners have shown strong potential for autonomous driving by capturing multi-modal driving behaviors. A key challenge is how to effectively guide these models for…
4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos
Shanshan Zhong, Jiawei Peng, Zehan Zheng +6
Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is lab…
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
Zhongzhan Huang, Guoming Ling, Shanshan Zhong +2
Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existi…
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
Zhongzhan Huang, Guoming Ling, Yupei Lin +4
Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input. In this paper, our comprehensive an…
A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models
Zhongzhan Huang, Shanshan Zhong, Pan Zhou +3
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative cap…
MoExtend: Tuning New Experts for Modality and Task Extension
Shanshan Zhong, Shanghua Gao, Zhongzhan Huang +3
Large language models (LLMs) excel in various tasks but are primarily trained on text data, limiting their application scope. Expanding LLM capabilities to include vision-language…
AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
Yuanfeng Xu, Yuhao Chen, Zhongzhan Huang +4
Recent video editing advancements rely on accurate pose sequences to animate subjects. However, these efforts are not suitable for cross-species animation due to pose misalignment…
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
Shanshan Zhong, Zhongzhan Huang, Daifeng Li +3
Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The inte…
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
Shanshan Zhong, Zhongzhan Huang, Shanghua Gao +4
Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not c…
ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
Zhongzhan Huang, Pan Zhou, Shuicheng Yan +1
In diffusion models, UNet is the most popular network backbone, since its long skip connects (LSCs) to connect distant network blocks can aggregate long-distant information and all…
Understanding Self-attention Mechanism via Dynamical System Perspective
Zhongzhan Huang, Mingfu Liang, Jinghui Qin +2
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models. However, current ex…
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
Shanshan Zhong, Wushao Wen, Jinghui Qin +2
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel region…
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Shanshan Zhong, Zhongzhan Huang, Wushao Wen +2
Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there a…
ASR: Attention-alike Structural Re-parameterization
Shanshan Zhong, Zhongzhan Huang, Wushao Wen +2
The structural re-parameterization (SRP) technique is a novel deep learning technique that achieves interconversion between different network architectures through equivalent param…
AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios
Zhongzhan Huang, Mingfu Liang, Shanshan Zhong +1
We propose the attention-inspired numerical solver (AttNS), a concise method that helps the generalization and robustness issues faced by the AI-Hybrid numerical solver in solving…
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy
Shanshan Zhong, Wushao Wen, Jinghui Qin +1
More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings. However, deepening…