Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Kouichi SakuraiSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works77 from public data
- Personalize Segment Anything Model with One Shot400
A training-free Personalization approach for SAM, termed as PerSAM, which effectively adapt SAM for private use without any training, and can enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning.
- 383
- ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation264
This work introduces an innovative approach for robot manipulation that leverages the robust reasoning capabilities of Multimodal Large Language Models (MLLMs) to enhance the stability and generalization of manipulation.
- SpatialBot: Precise Spatial Understanding with Vision Language Models220
SpatialBot, a model designed to enhance spatial understanding by utilizing both RGB and depth images, is proposed and the SpatialQA and SpatialQA $\boldsymbol{E}$ datasets are introduced to train VLMs for depth perception.
- Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation204
By closing the loop between vision and action, the end-to-end PIDM can be a better scalable action learner, and is initially pre-trained on large-scale robotic datasets, such as DROID, and can be adapted to realworld scenarios with a little fine-tuning data.
- Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model177
This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks, employing the LLM model to generate Python programs that constitute a comprehensive perception, planning, and action loop for robotic tasks.
- Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill151
This work proposes a Pixel-guided Navigation skill (PixNav), which bridges the gap between the foundation models and the embodied navigation task, and designs an LLM-based planner to utilize the commonsense knowledge between objects and rooms to select the best waypoint.
- GFPose: Learning 3D Human Pose Prior with Gradient Fields102
GFPose is a versatile framework to model plausible 3D human poses for various applications, which is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed3D human pose to match a given task specification.
- Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks92
This work proposes three types of basic skills in Minecraft, and uses RL with intrinsic rewards to accomplish basic skills with high success rates and outperforms baselines in most tasks by a large margin.
- SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization80
This paper argues that mapping features from different modalities into the same embedding space impedes model generalization and proposes SimMMDG, a simple yet effective multi-modal DG framework that achieves strong performance on the EPIC-Kitchens dataset and the novel Human-Animal-Cartoon dataset.
- 75
- A Survey of Reasoning with Foundation Models73
This paper introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks and discusses the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning.
- ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models57
ManipVQA is proposed, a novel framework that infuses MLLMs with manipulation-centric knowledge through a Visual Question-Answering (VQA) format and preserves the original vision-reasoning abilities while incorporating the newly acquired robotic insights.
- 1002 km twin-field quantum key distribution with finite-key analysis43
By optimizing parameters for shorter fiber distances, the feasibility of secure QKD over a 1002 km fibre channel considering the finite size effect is demonstrated and performance tests on key distribution for fiber lengths ranging from 202 km to 505 km are conducted.
- SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation37
SarseDFF is presented, a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic features from sparse RGBD images, a domain where research is limited despite its relevance to many tasks with fixed-camera setups.
- Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL35
A novel framework named Heterogeneous-Agent Mirror Learning (HAML) is introduced that provides a general template for MARL algorithmic designs and it is proved that algorithms derived from the HAML template satisfy the desired properties of the monotonic improvement of the joint reward and the convergence to Nash equilibrium.
- 31
- 30
- Improving Compositional Text-to-image Generation with Large Vision-Language Models30
The proposed methodology significantly improves text-image alignment in compositional image generation, particularly with respect to object number, attribute binding, spatial relationships, and aesthetic quality.
- 27
- 27
- Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision25
This work introduces a novel approach to address Multimodal Open-Set Domain Generalization for the first time, utilizing self-supervision, and introduces two innovative multimodal self-supervised pretext tasks: Masked Cross-modal Translation and Multimodal Jigsaw Puzzles.
- 25
- A repeated game approach for analyzing the collusion on selective forwarding in multihop wireless networks22
This paper first puts forward a sub-route oriented punish and reward scheme, and proposes an multi-attacker repeated colluding game, and finds the sub-game equilibriums which indicate the attackers' optimal attack strategies.
- Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification19
A deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN) is developed, which proposes a hierarchical transformer to extract the textual semantic information of proposals and designs a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.