637 citations · 875 across the 23 of their papers we have counts for
8 papers · 1 filter
Fewer is More: Efficient Object Detection in Large Aerial Images
Xingxing Xie, Gong Cheng, Qingyang Li +3
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no…
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data
Xiao Li, Yin Zhu, Sichen Liu +3
Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program co…
Knowledge Base Question Answering: A Semantic Parsing Perspective
Yu Gu, Vardaan Pahuja, Gong Cheng +1
Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language inter…
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
Dongzhuoran Zhou, Baifan Zhou, Jieying Chen +3
Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important appr…
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Gong Cheng, Xiang Yuan, Xiwen Yao +4
With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactor…
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation
Chunbo Lang, Binfei Tu, Gong Cheng +1
Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing ap…
Learning What Not to Segment: A New Perspective on Few-Shot Segmentation
Chunbo Lang, Gong Cheng, Binfei Tu +1
Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classific…
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
Xiao Li, Gong Cheng, Ziheng Chen +2
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reas…