Zobrazeno 1 - 10
of 4 476
pro vyhledávání: '"An, Yadan"'
Dataset Distillation (DD) is designed to generate condensed representations of extensive image datasets, enhancing training efficiency. Despite recent advances, there remains considerable potential for improvement, particularly in addressing the nota
Externí odkaz:
http://arxiv.org/abs/2411.11329
In the Detection and Multi-Object Tracking of Sweet Peppers Challenge, we present Track Any Peppers (TAP) - a weakly supervised ensemble technique for sweet peppers tracking. TAP leverages the zero-shot detection capabilities of vision-language found
Externí odkaz:
http://arxiv.org/abs/2411.06702
Contrastive language-image pre-training (CLIP) has shown remarkable generalization ability in image classification. However, CLIP sometimes encounters performance drops on downstream datasets during zero-shot inference. Test-time adaptation methods a
Externí odkaz:
http://arxiv.org/abs/2410.14729
Autor:
Chen, Zhi, Wei, Tianqi, Zhao, Zecheng, Lim, Jia Syuen, Luo, Yadan, Zhang, Hu, Yu, Xin, Chapman, Scott, Huang, Zi
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting. This paper addresses the challenge of reconstructing accurate 3D shapes of fruits from partial views, wh
Externí odkaz:
http://arxiv.org/abs/2409.08443
Solar filaments can undergo eruptions and result in the formation of coronal mass ejections (CMEs), which could significantly impact planetary space environments. Observations of eruptions involving polar crown filaments, situated in the polar region
Externí odkaz:
http://arxiv.org/abs/2408.15892
LiDAR-based outdoor 3D object detection has received widespread attention. However, training 3D detectors from the LiDAR point cloud typically relies on expensive bounding box annotations. This paper presents SC3D, an innovative label-efficient metho
Externí odkaz:
http://arxiv.org/abs/2408.08092
Conventional Text-guided single-image editing approaches require a two-step process, including fine-tuning the target text embedding for over 1K iterations and the generative model for another 1.5K iterations. Although it ensures that the resulting i
Externí odkaz:
http://arxiv.org/abs/2408.03355
Autor:
Lim, Jia Syuen, Chen, Zhuoxiao, Baktashmotlagh, Mahsa, Chen, Zhi, Yu, Xin, Huang, Zi, Luo, Yadan
Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many downstream vision tasks. Despite considerable advancements in bottom-up and multi-object discovery methods that leverage basic visual cues to identify salient objects,
Externí odkaz:
http://arxiv.org/abs/2406.14924
LiDAR-based 3D object detection is crucial for various applications but often experiences performance degradation in real-world deployments due to domain shifts. While most studies focus on cross-dataset shifts, such as changes in environments and ob
Externí odkaz:
http://arxiv.org/abs/2406.14878
LiDAR-based 3D object detection has seen impressive advances in recent times. However, deploying trained 3D detectors in the real world often yields unsatisfactory performance when the distribution of the test data significantly deviates from the tra
Externí odkaz:
http://arxiv.org/abs/2406.13891