Search Results for author:
Found 15 papers, 5 papers with code
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model
APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV.
PhysPart: Physically Plausible Part Completion for Interactable Objects
We also demonstrate our applications in 3D printing, robot manipulation, and sequential part generation, showing our strength in realistic tasks with the demand for high physical plausibility.
R2G: Reasoning to Ground in 3D Scenes
We propose Reasoning to Ground (R2G), a neural symbolic model that grounds the target objects within 3D scenes in a reasoning manner.
Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
Despite significant advancements in text-to-motion synthesis, generating language-guided human motion within 3D environments poses substantial challenges.
Impart: An Imperceptible and Effective Label-Specific Backdoor Attack
Backdoor attacks have been shown to impose severe threats to real security-critical scenarios.
Scaling Up Dynamic Human-Scene Interaction Modeling
Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling, we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method.
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions.
Ranked #1 on
Grounded Multimodal Named Entity Recognition
on Twitter-GMNER
(using extra training data)
Grounded Multimodal Named Entity Recognition
Multi-modal Named Entity Recognition
+8
Move as You Say Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
Despite significant advancements in text-to-motion synthesis generating language-guided human motion within 3D environments poses substantial challenges.
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine
To the best of our knowledge, our study represents the pioneering validation of domain adaptation of a large language model with 7 billion parameters in TCM domain.
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding.
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes
Learning to generate diverse scene-aware and goal-oriented human motions in 3D scenes remains challenging due to the mediocre characteristics of the existing datasets on Human-Scene Interaction (HSI); they only have limited scale/quality and lack semantics.
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation
To address this, we introduce a notion of the exposure risk and propose a novel problem of attacking a history news dataset by means of perturbations where the goal is to maximize the manipulation of the target news rank while keeping the risk of exposure under a given budget.
PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene Rearrangement Planning
Scene Rearrangement Planning (SRP) is an interior task proposed recently.
An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems
Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework.
