5 citations · 14 across the 21 of their papers we have counts for
25 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…
AudioLens: Multi-Perspective Speech Clustering with Reasoning Audio-Language Models
Wenjun Huang, Qiaosong Chu, Tiger Shao +11
Audio clustering is a fundamental task for organizing rapidly growing speech collections, supporting applications such as conversational analysis and speech-driven discovery. Howev…
FusionSense: Tri-Stage Near-Sensor Learning for Runtime-Adaptive Multimodal Edge Intelligence
Sanggeon Yun, Ryozo Masukawa, Minhyoung Na +5
Autonomous systems and smart-industry deployments increasingly split computation across near-sensor, edge, and cloud resources, where tight energy, latency, and reliability budgets…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
-Musketeers: Reinforcement Learning Shapes Collaboration Among Language Models
Ryozo Masukawa, Sanggeon Yun, Hyunwoo Oh +8
Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on la…
Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models
Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong +3
Vision-Language Models (VLMs) such as CLIP enable strong zero-shot recognition but suffer substantial degradation under distribution shifts. Test-Time Adaptation (TTA) aims to impr…
Cauchy-Schwarz Fairness Regularizer
Yezi Liu, Hanning Chen, Wenjun Huang +2
Group fairness in machine learning is often enforced by adding a regularizer that reduces the dependence between model predictions and sensitive attributes. However, existing regul…
LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples
Yezi Liu, Hanning Chen, Wenjun Huang +2
Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes i…
Recover-to-Forget: Gradient Reconstruction from LoRA for Efficient LLM Unlearning
Yezi Liu, Hanning Chen, Wenjun Huang +2
Unlearning in large foundation models (e.g., LLMs) is essential for enabling dynamic knowledge updates, enforcing data deletion rights, and correcting model behavior. However, exis…
QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention
Hyunwoo Oh, Hanning Chen, Sanggeon Yun +5
Deformable transformers deliver state-of-the-art detection but map poorly to hardware due to irregular memory access and low arithmetic intensity. We introduce QUILL, a schedule-aw…
IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation
Yifan Li, Lichi Li, Anh Dao +15
While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks la…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…
Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing
Yezi Liu, Prathyush Poduval, Wenjun Huang +3
Graph unlearning is a crucial approach for protecting user privacy by erasing the influence of user data on trained graph models. Recent developments in graph unlearning methods ha…
LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation
Hanning Chen, Yang Ni, Wenjun Huang +4
Large Vision Language Models (LVLMs) have been widely adopted to guide vision foundation models in performing reasoning segmentation tasks, achieving impressive performance. Howeve…
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong +5
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with nat…
Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge
Sanggeon Yun, Ryozo Masukawa, Hanning Chen +6
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computation…
Tell Me What to Track: Infusing Robust Language Guidance for Enhanced Referring Multi-Object Tracking
Wenjun Huang, Yang Ni, Hanning Chen +4
Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to localize an arbitrary number of targets based on a language expression and continuously track th…
Expanding Event Modality Applications through a Robust CLIP-Based Encoder
Sungheon Jeong, Hanning Chen, Sanggeon Yun +4
This paper introduces a powerful encoder that transfers CLIP`s capabilities to event-based data, enhancing its utility and expanding its applicability across diverse domains. While…
VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation
Hanning Chen, Yang Ni, Wenjun Huang +6
Vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a s…
Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach
Wenjun Huang, Yang Ni, Arghavan Rezvani +5
Human pose estimation (HPE) is crucial for various applications. However, deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential…
EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
Wenjun Huang, Hanning Chen, Yang Ni +5
Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment. We propose a difficulty-aware edge-cloud collaborative…
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection
Hanning Chen, Wenjun Huang, Yang Ni +6
Task-oriented object detection aims to find objects suitable for accomplishing specific tasks. As a challenging task, it requires simultaneous visual data processing and reasoning…
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
Yang Ni, Zhuowen Zou, Wenjun Huang +6
Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dime…
A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission
Wenjun Huang, Arghavan Rezvani, Hanning Chen +4
Applications in the Internet of Things (IoT) utilize machine learning to analyze sensor-generated data. However, a major challenge lies in the lack of targeted intelligence in curr…