1 citations · 1 across the 15 of their papers we have counts for
15 papers
AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention
Juncheng Liao, Jinfan Lv, Guoming Wang +3
Multimodal Large Language Models (MLLMs) exhibit strong generalization and reasoning abilities due to large-scale multimodal pre-training. However, fine-tuning these models on down…
Dataset Distillation Based on Saliency-Driven Prototype Alignment
Yawen Zou, Wenqi Cai, Guang Li +3
Dataset distillation aims to synthesize compact datasets that can approximate the performance of full-data training while significantly reducing computational and storage costs. Ho…
HumAIN: Human-Aware Implicit Social Robot Navigation
Daeun Song, Nhat Le, Jeffrey Chen +7
Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Soc…
Vision-Language Models for Deployable Social Robot Navigation: Bridging Semantic Reasoning and Low-Level Control
Runji Cai, Toshihiko Yamasaki, Ling Xiao
Social robot navigation (SRN) requires more than geometric path planning; it demands understanding human intentions, social norms, and contextual cues to generate socially complian…
Rethinking Groups in Critic-Free RLVR
Yihong Wu, Liheng Ma, Lingfeng Xiao +4
Reinforcement learning (RL) has become a central paradigm for post-training large language models. Existing critic-free RL methods typically generate a group of rollouts for the sa…
E-SocialNav: Efficient Socially Compliant Navigation with Language Models
Ling Xiao, Daeun Song, Xuesu Xiao +1
Language models (LMs) are increasingly applied to robotic navigation; however, existing benchmarks primarily emphasize navigation success rates while paying limited attention to so…
A Multihead Continual Learning Framework for Fine-Grained Fashion Image Retrieval with Contrastive Learning and Exponential Moving Average Distillation
Ling Xiao, Toshihiko Yamasaki
Most fine-grained fashion image retrieval (FIR) methods assume a static setting, requiring full retraining when new attributes appear, which is costly and impractical for dynamic s…
Enhancing Lightweight Vision Language Models through Group Competitive Learning for Socially Compliant Navigation
Xinyu Zhang, Atsushi Konno, Toshihiko Yamasaki +1
Social robot navigation requires a sophisticated integration of scene semantics and human social norms. Scaling up Vision Language Models (VLMs) generally improves reasoning and de…
Spectral Probing of Feature Upsamplers in 2D-to-3D Scene Reconstruction
Ling Xiao, Yuliang Xiu, Yue Chen +2
A typical 2D-to-3D pipeline takes multi-view images as input, where a Vision Foundation Model (VFM) extracts features that are spatially upsampled to dense representations for 3D r…
Probing Prompt Design for Socially Compliant Robot Navigation with Vision Language Models
Ling Xiao, Toshihiko Yamasaki
Language models are increasingly used for social robot navigation, yet existing benchmarks largely overlook principled prompt design for socially compliant behavior. This limitatio…
LLM-Advisor: An LLM Advisor for Cost-efficient Path Planning across Multiple Terrains
Ling Xiao, Toshihiko Yamasaki
This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while m…
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
Zerun Wang, Liuyu Xiang, Lang Huang +3
Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) s…
Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
Tomoya Sugihara, Shuntaro Masuda, Ling Xiao +1
Current video summarization methods rely heavily on supervised computer vision techniques, which demands time-consuming and subjective manual annotations. To overcome these limitat…
Improving Plasticity in Online Continual Learning via Collaborative Learning
Maorong Wang, Nicolas Michel, Ling Xiao +1
Online Continual Learning (CL) solves the problem of learning the ever-emerging new classification tasks from a continuous data stream. Unlike its offline counterpart, in online CL…
Online Open-set Semi-supervised Object Detection with Dual Competing Head
Zerun Wang, Ling Xiao, Liuyu Xiang +2
Open-set semi-supervised object detection (OSSOD) task leverages practical open-set unlabeled datasets that comprise both in-distribution (ID) and out-of-distribution (OOD) instanc…