activity
20162024
most citedDiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images

74 citations · 166 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV202474 cited

DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images

Zhou Jie, Xiao Chao, Peng Bo +4

Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-…

cs.CL2024

Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering

Baiyan Zhang, Qin Chen, Jie Zhou +2

Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to…

cs.IR2023

Plot Retrieval as an Assessment of Abstract Semantic Association

Shicheng Xu, Liang Pang, Jiangnan Li +5

Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract an…

cs.CL20237 cited

Improving Translation Faithfulness of Large Language Models via Augmenting Instructions

Yijie Chen, Yijin Liu, Fandong Meng +3

Large Language Models (LLMs) present strong general capabilities, and a current compelling challenge is stimulating their specialized capabilities, such as machine translation, thr…

cs.CL2023

Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias

Zhiyuan Zhang, Deli Chen, Hao Zhou +3

Pre-trained Language Models (PLMs) may be poisonous with backdoors or bias injected by the suspicious attacker during the fine-tuning process. A core challenge of purifying potenti…

cs.CL201668 cited

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

Peng Li, Wei Li, Zhengyan He +4

While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge…