28 citations · 62 across the 10 of their papers we have counts for
10 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…
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,…
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…
Neural Person Search Machines
Hao Liu, Jiashi Feng, Zequn Jie +5
We investigate the problem of person search in the wild in this work. Instead of comparing the query against all candidate regions generated in a query-blind manner, we propose to…
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…