1 citations · 1 across the 12 of their papers we have counts for
12 papers
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…
Video-Mirai: Autoregressive Video Diffusion Models Need Foresight
Yonghao Yu, Lang Huang, Runyi Li +2
Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segment becomes a commitment tha…
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…
Reward Incremental Learning in Text-to-Image Generation
Maorong Wang, Jiafeng Mao, Xueting Wang +1
The recent success of denoising diffusion models has significantly advanced text-to-image generation. While these large-scale pretrained models show excellent performance in genera…
Dealing with Synthetic Data Contamination in Online Continual Learning
Maorong Wang, Nicolas Michel, Jiafeng Mao +1
Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of…
Difficulty Controlled Diffusion Model for Synthesizing Effective Training Data
Zerun Wang, Jiafeng Mao, Xueting Wang +1
Generative models have become a powerful tool for synthesizing training data in computer vision tasks. Current approaches solely focus on aligning generated images with the target…
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…
From Obstacles to Resources: Semi-supervised Learning Faces Synthetic Data Contamination
Zerun Wang, Jiafeng Mao, Liuyu Xiang +1
Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synth…
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…