12 citations · 26 across the 16 of their papers we have counts for
16 papers
DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Yanqi Wu, Xinhua Lu, Runhe Lai +4
Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in vision-language models (VLMs). Ho…
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…
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…
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…
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…
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…
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
Yipeng Gao, Zeyu Wang, Wei-Shi Zheng +2
Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually.…
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…
ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation
Shenghao Fu, Junkai Yan, Yipeng Gao +2
Recent sparse detectors with multiple, e.g. six, decoder layers achieve promising performance but much inference time due to complex heads. Previous works have explored using dense…
Event-Guided Procedure Planning from Instructional Videos with Text Supervision
An-Lan Wang, Kun-Yu Lin, Jia-Run Du +2
In this work, we focus on the task of procedure planning from instructional videos with text supervision, where a model aims to predict an action sequence to transform the initial…
Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
Jiawei Feng, Ancong Wu, Wei-Shi Zheng
Due to the modality gap between visible and infrared images with high visual ambiguity, learning \textbf{diverse} modality-shared semantic concepts for visible-infrared person re-i…
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
Jiayu Jiao, Yu-Ming Tang, Kun-Yu Lin +4
As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive f…
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition
Peipei Zhu, Xiao Wang, Yong Luo +4
The goal of unpaired image captioning (UIC) is to describe images without using image-caption pairs in the training phase. Although challenging, we except the task can be accomplis…