4 citations · 6 across the 18 of their papers we have counts for
19 papers
ProEdit: Inversion-based Editing From Prompts Done Right
Zhi Ouyang, Dian Zheng, Xiao-Ming Wu +4
Inversion-based visual editing provides an effective and training-free way to edit an image or a video based on user instructions. Existing methods typically inject source image in…
CoopDiff: Anticipating 3D Human-object Interactions via Contact-consistent Decoupled Diffusion
Xiaotong Lin, Tianming Liang, Jian-Fang Hu +5
3D human-object interaction (HOI) anticipation aims to predict the future motion of humans and their manipulated objects, conditioned on the historical context. Generally, the arti…
Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization
Jiaming Zhou, Ke Ye, Jiayi Liu +6
The generalization capabilities of vision-language-action (VLA) models to unseen tasks are crucial to achieving general-purpose robotic manipulation in open-world settings. However…
ActionArt: Advancing Multimodal Large Models for Fine-Grained Human-Centric Video Understanding
Yi-Xing Peng, Qize Yang, Yu-Ming Tang +4
Fine-grained understanding of human actions and poses in videos is essential for human-centric AI applications. In this work, we introduce ActionArt, a fine-grained video-caption d…
Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks
Wei-Jin Huang, Yuan-Ming Li, Zhi-Wei Xia +4
Error detection in procedural activities is essential for consistent and correct outcomes in AR-assisted and robotic systems. Existing methods often focus on temporal ordering erro…
Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks
Yu Zhou, Dian Zheng, Qijie Mo +3
In this work, we present DEcoupLEd Distillation To Erase (DELETE), a general and strong unlearning method for any class-centric tasks. To derive this, we first propose a theoretica…
ViSpeak: Visual Instruction Feedback in Streaming Videos
Shenghao Fu, Qize Yang, Yuan-Ming Li +6
Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent mod…
Task-Oriented 6-DoF Grasp Pose Detection in Clutters
An-Lan Wang, Nuo Chen, Kun-Yu Lin +2
In general, humans would grasp an object differently for different tasks, e.g., "grasping the handle of a knife to cut" vs. "grasping the blade to hand over". In the field of robot…
ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations
Tianming Liang, Kun-Yu Lin, Chaolei Tan +3
Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. This is challenging as it involves deep vision-language un…
TechCoach: Towards Technical-Point-Aware Descriptive Action Coaching
Yuan-Ming Li, An-Lan Wang, Kun-Yu Lin +4
To guide a learner in mastering action skills, it is crucial for a coach to 1) reason through the learner's action execution and technical points (TechPoints), and 2) provide detai…
Towards Completeness: A Generalizable Action Proposal Generator for Zero-Shot Temporal Action Localization
Jia-Run Du, Kun-Yu Lin, Jingke Meng +1
To address the zero-shot temporal action localization (ZSTAL) task, existing works develop models that are generalizable to detect and classify actions from unseen categories. They…
ParGo: Bridging Vision-Language with Partial and Global Views
An-Lan Wang, Bin Shan, Wei Shi +7
This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous work…
Human-Centric Transformer for Domain Adaptive Action Recognition
Kun-Yu Lin, Jiaming Zhou, Wei-Shi Zheng
We study the domain adaptation task for action recognition, namely domain adaptive action recognition, which aims to effectively transfer action recognition power from a label-suff…
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
Jiaming Zhou, Teli Ma, Kun-Yu Lin +3
Learning generalizable visual representations across different embodied environments is essential for effective robotic manipulation in real-world scenarios. However, the limited s…
XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition
Kun-Yu Lin, Henghui Ding, Jia-Run Du +6
Inspired by the impressive success of image-text foundation models, recent works have proposed to adapt these foundation models to video data, leading to efficient and effective vi…
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition
Jiaming Zhou, Junwei Liang, Kun-Yu Lin +2
Zero-shot action recognition (ZSAR) aims to learn an alignment model between videos and class descriptions of seen actions that is transferable to unseen actions. The text queries…
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition
Jiaming Zhou, Hanjun Li, Kun-Yu Lin +1
Developing end-to-end action recognition models on long videos is fundamental and crucial for long-video action understanding. Due to the unaffordable cost of end-to-end training o…
Distilling the Unknown to Unveil Certainty
Zhilin Zhao, Longbing Cao, Yixuan Zhang +2
Out-of-distribution (OOD) detection is critical for identifying test samples that deviate from in-distribution (ID) data, ensuring network robustness and reliability. This paper pr…
Diversifying Spatial-Temporal Perception for Video Domain Generalization
Kun-Yu Lin, Jia-Run Du, Yipeng Gao +2
Video domain generalization aims to learn generalizable video classification models for unseen target domains by training in a source domain. A critical challenge of video domain g…