4 citations · 6 across the 9 of their papers we have counts for
13 papers
ADAPT: Audio-Depth Action Planning with Transition-Aware Rewards for Lights-Out Industrial Navigation
Wenjun Huang, Shenghao Fu, Yian Jin +12
Vision-language-action models for embodied navigation typically rely on RGB observations, limiting their applicability in lights-out industrial environments where visible illuminat…
PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion
Lehan Yang, Daiqing Qi, Wenhao Zhang +9
Representation alignment (REPA) accelerates diffusion transformer training, but its alignment targets are almost exclusively semantic encoders such as DINOv2 and CLIP. Recent analy…
ViT-AdaLA: Adapting Vision Transformers with Linear Attention
Yifan Li, Seunghyun Yoon, Viet Dac Lai +4
Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits…
Open Set Face Forgery Detection via Dual-Level Evidence Collection
Zhongyi Cai, Bryce Gernon, Wentao Bao +3
The surge in face forgeries has increasingly undermined confidence in the authenticity of online content. As generation algorithms rapidly evolve, new fake categories will constant…
IndustryNav: Evaluating Spatial Reasoning and Safety of Embodied Agents in Dynamic Industrial Navigation
Yifan Li, Lichi Li, Anh Dao +15
Visual large language models (VLLMs) are increasingly used as embodied agents, yet industrial warehouses demand more than reaching a goal: agents must navigate safely around moving…
ViT-Split: Unleashing the Power of Vision Foundation Models via Efficient Splitting Heads
Yifan Li, Xin Li, Tianqin Li +3
Vision foundation models (VFMs) have demonstrated remarkable performance across a wide range of downstream tasks. While several VFM adapters have shown promising results by leverag…
IndustryEQA: Pushing the Frontiers of Embodied Question Answering in Industrial Scenarios
Yifan Li, Yuhang Chen, Anh Dao +5
Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to indu…
Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
Zhen Tan, Song Wang, Yifan Li +4
Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how the inputs lead to the predicti…
Window Token Concatenation for Efficient Visual Large Language Models
Yifan Li, Wentao Bao, Botao Ye +4
To effectively reduce the visual tokens in Visual Large Language Models (VLLMs), we propose a novel approach called Window Token Concatenation (WiCo). Specifically, we employ a sli…
Visual Large Language Models for Generalized and Specialized Applications
Yifan Li, Zhixin Lai, Wentao Bao +7
Visual-language models (VLM) have emerged as a powerful tool for learning a unified embedding space for vision and language. Inspired by large language models, which have demonstra…
Facial Affective Behavior Analysis with Instruction Tuning
Yifan Li, Anh Dao, Wentao Bao +4
Facial affective behavior analysis (FABA) is crucial for understanding human mental states from images. However, traditional approaches primarily deploy models to discriminate amon…
The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative
Zhen Tan, Chengshuai Zhao, Raha Moraffah +4
Due to their unprecedented ability to process and respond to various types of data, Multimodal Large Language Models (MLLMs) are constantly defining the new boundary of Artificial…
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Yifan Li, Zhen Tan, Kai Shu +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when t…