Search Results for author:
Found 15 papers, 3 papers with code
MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception
Perception plays a crucial role in various robot applications.
Reliable Spatial-Temporal Voxels For Multi-Modal Test-Time Adaptation
Multi-modal test-time adaptation (MM-TTA) is proposed to adapt models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner.
MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
In this work, we propose Multi-modal Prior Aided (MoPA) domain adaptation to improve the performance of rare objects.
Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation
In this paper, we explore Multi-Modal Continual Test-Time Adaptation (MM-CTTA) as a new extension of CTTA for 3D semantic segmentation.
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models.
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
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 improve the robustness and transferability of video models.
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments.
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
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 challenging environments in real-world scenarios are still under-explored.
Self-Supervised Video Representation Learning by Video Incoherence Detection
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
Aligning Correlation Information for Domain Adaptation in Action Recognition
Yet correlation features of the same action would differ across domains due to domain shift.
Effective Action Recognition with Embedded Key Point Shifts
Temporal feature extraction is an essential technique in video-based action recognition.
PNL: Efficient Long-Range Dependencies Extraction with Pyramid Non-Local Module for Action Recognition
Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83. 09% on the Mini-Kinetics dataset, with decreased computation cost compared to the non-local block.
ARID: A New Dataset for Recognizing Action in the Dark
We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset.
