1 code implementation • ACL 2022 • Biru Zhu, Yujia Qin, Fanchao Qi, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
To validate our viewpoints, we design two methods to evaluate the robustness of FMS: (1) model disguise attack, which post-trains an inferior PTM with a contrastive objective, and (2) evaluation data selection, which selects a subset of the data points for FMS evaluation based on K-means clustering.
no code implementations • 10 Apr 2024 • Longwei Zou, Qingyang Wang, Han Zhao, Jiangang Kong, Yi Yang, Yangdong Deng
The fast-growing large scale language models are delivering unprecedented performance on almost all natural language processing tasks.
1 code implementation • 7 Apr 2024 • Longwei Zou, Han Zhang, Yangdong Deng
Specifically, the framework is based on three basic operators, Coalescing, De-coalescing and Interpolation, which can be orchestrated to build a multi-level training framework.
1 code implementation • 9 Sep 2023 • Fanling Huang, Yangdong Deng
In this work, we propose a novel data-driven model, Multi-Horizon SpatioTemporal Network (MHSTN), generally for accurate and efficient fine-grained wind prediction.
1 code implementation • 9 Sep 2023 • Fanling Huang, Yangdong Deng
Nonetheless, to our best knowledge, it remains unclear how effectively GANs can serve as a general-purpose solution to learn representations for time series recognition, i. e., classification and clustering.
no code implementations • ECCV 2018 • Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun
(1) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales.
2 code implementations • 17 Apr 2018 • Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun
Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection.
5 code implementations • 20 Nov 2017 • Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun
More importantly, simply replacing the backbone with a tiny network (e. g, Xception), our Light-Head R-CNN gets 30. 7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.