15 citations · 16 across the 10 of their papers we have counts for
10 papers
Teach-DETR: Better Training DETR with Teachers
Linjiang Huang, Kaixin Lu, Guanglu Song +4
In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors. We show that the predicted boxes from t…
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Wentao Chen, Zhang Zhang, Wei Wang +3
Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domai…
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
Penghui Wei, Shaoguo Liu, Xuanhua Yang +2
Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle cr…
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Qiang Liu, Yingtao Luo, Shu Wu +4
In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejecte…
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
Penghui Wei, Xuanhua Yang, Shaoguo Liu +2
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). W…
Deeply Explain CNN via Hierarchical Decomposition
Ming-Ming Cheng, Peng-Tao Jiang, Ling-Hao Han +2
In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect the network prediction. However, they usually ignore the feat…