36 citations · 101 across the 14 of their papers we have counts for
14 papers
ViReSkill: Vision-Grounded Replanning with Skill Memory for LLM-Based Planning in Lifelong Robot Learning
Tomoyuki Kagaya, Subramanian Lakshmi, Anbang Ye +6
Robots trained via Reinforcement Learning (RL) or Imitation Learning (IL) often adapt slowly to new tasks, whereas recent Large Language Models (LLMs) and Vision-Language Models (V…
Memory Transfer Planning: LLM-driven Context-Aware Code Adaptation for Robot Manipulation
Tomoyuki Kagaya, Subramanian Lakshmi, Yuxuan Lou +6
Large language models (LLMs) are increasingly explored in robot manipulation, but many existing methods struggle to adapt to new environments. Many systems require either environme…
Aligned Contrastive Loss for Long-Tailed Recognition
Jiali Ma, Jiequan Cui, Maeno Kazuki +4
In this paper, we propose an Aligned Contrastive Learning (ACL) algorithm to address the long-tailed recognition problem. Our findings indicate that while multi-view training boost…
Unbiased Regression Loss for DETRs
Edric, Ueta Daisuke, Kurokawa Yukimasa +2
In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional regression loss tends to bias towards larger boxes, as they dispropo…
EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI
Tomoyuki Kagaya, Yuxuan Lou, Thong Jing Yuan +8
In recent years, Large Language Models (LLMs) have demonstrated high reasoning capabilities, drawing attention for their applications as agents in various decision-making processes…
Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation
Rashi Sharma, Hiroyuki Okada, Tatsumi Oba +3
The Industrial Control System (ICS) environment encompasses a wide range of intricate communication protocols, posing substantial challenges for Security Operations Center (SOC) an…
RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents
Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou +6
Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming,…
Invariant Feature Regularization for Fair Face Recognition
Jiali Ma, Zhongqi Yue, Kagaya Tomoyuki +4
Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalan…
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
Tan Wang, Qianru Sun, Sugiri Pranata +2
We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints. First, compared to sufficient data, we show why ins…
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation
Jayashree Karlekar, Jiashi Feng, Zi Sian Wong +1
Fully convolutional networks (FCNs) have become de facto tool to achieve very high-level performance for many vision and non-vision tasks in general and face recognition in particu…
Anomaly Detection with Adversarial Dual Autoencoders
Ha Son Vu, Daisuke Ueta, Kiyoshi Hashimoto +3
Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somew…
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Jian Zhao, Yu Cheng, Yi Cheng +12
Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face chang…
Person re-identification with fusion of hand-crafted and deep pose-based body region features
Jubin Johnson, Shunsuke Yasugi, Yoichi Sugino +2
Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras. Existing works learn deep represen…
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
Lin Xiong, Jayashree Karlekar, Jian Zhao +5
Unconstrained face recognition performance evaluations have traditionally focused on Labeled Faces in the Wild (LFW) dataset for imagery and the YouTubeFaces (YTF) dataset for vide…