40 citations · 113 across the 31 of their papers we have counts for
24 papers · 1 filter
Multi-Scale Temporal Domain Alignment for Federated Video Domain Adaptation
Lee En-Yi Hannah, Haozhi Cao, Yuecong Xu
Federated Video Domain Adaptation (FVDA) enables collaborative learning across distributed and non-IID video datasets while preserving privacy, but is under-explored due to challen…
Semantic Surgery: Zero-Shot Concept Erasure in Diffusion Models
Lexiang Xiong, Chengyu Liu, Jingwen Ye +2
Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery…
Minute-Long Videos with Dual Parallelisms
Zeqing Wang, Bowen Zheng, Xingyi Yang +3
Diffusion Transformer (DiT)-based video diffusion models generate high-quality videos at scale but incur prohibitive processing latency and memory costs for long videos. To address…
Diffusion Model is a Good Pose Estimator from 3D RF-Vision
Junqiao Fan, Jianfei Yang, Yuecong Xu +1
Human pose estimation (HPE) from Radio Frequency vision (RF-vision) performs human sensing using RF signals that penetrate obstacles without revealing privacy (e.g., facial informa…
Interactive Test-Time Adaptation with Reliable Spatial-Temporal Voxels for Multi-Modal Segmentation
Haozhi Cao, Yuecong Xu, Pengyu Yin +4
Multi-modal test-time adaptation (MM-TTA) adapts models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner. While previous MM-TTA…
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
Jianfei Yang, Shijie Tang, Yuecong Xu +2
Human activity recognition (HAR) has been playing an increasingly important role in various domains such as healthcare, security monitoring, and metaverse gaming. Though numerous H…
MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Multi-modal unsupervised domain adaptation (MM-UDA) for 3D semantic segmentation is a practical solution to embed semantic understanding in autonomous systems without expensive poi…
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?
Jianfei Yang, Hanjie Qian, Yuecong Xu +2
Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain. However, in real-world scenarios, the absence of…
Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Continual Test-Time Adaptation (CTTA) generalizes conventional Test-Time Adaptation (TTA) by assuming that the target domain is dynamic over time rather than stationary. In this pa…
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Yunjiao Zhou +3
For video models to be transferred and applied seamlessly across video tasks in varied environments, Video Unsupervised Domain Adaptation (VUDA) has been introduced to improve the…
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Jianfei Yang +2
Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without…
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
Yuecong Xu, Haozhi Cao, Zhenghua Chen +3
Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction…
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to impr…
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Kezhi Mao +1
Assuming the source label space subsumes the target one, Partial Video Domain Adaptation (PVDA) is a more general and practical scenario for cross-domain video classification probl…
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environme…
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
Yuecong Xu, Jianfei Yang, Haozhi Cao +5
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to cha…
Self-Supervised Video Representation Learning by Video Incoherence Detection
Haozhi Cao, Yuecong Xu, Jianfei Yang +4
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It roots from the observation that visual systems of hu…
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios. It relaxes the assumption in conventional Unsupervised Domain Adaptati…
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsum…
Aligning Correlation Information for Domain Adaptation in Action Recognition
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent y…
Effective Action Recognition with Embedded Key Point Shifts
Haozhi Cao, Yuecong Xu, Jianfei Yang +3
Temporal feature extraction is an essential technique in video-based action recognition. Key points have been utilized in skeleton-based action recognition methods but they require…
PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
Yuecong Xu, Haozhi Cao, Jianfei Yang +3
Long-range spatiotemporal dependencies capturing plays an essential role in improving video features for action recognition. The non-local block inspired by the non-local means is…
ARID: A New Dataset for Recognizing Action in the Dark
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in the action recogn…
Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition
Yuecong Xu, Jianfei Yang, Kezhi Mao +2
Temporal feature extraction is an important issue in video-based action recognition. Optical flow is a popular method to extract temporal feature, which produces excellent performa…