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Found 10 papers, 5 papers with code
MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects
Embarking on this module, we propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects.
Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
Therefore, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.
Frequency Perception Network for Camouflaged Object Detection
Camouflaged object detection (COD) aims to accurately detect objects hidden in the surrounding environment.
Unifying Graph Contrastive Learning with Flexible Contextual Scopes
To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).
Exploring Explicit and Implicit Visual Relationships for Image Captioning
Image captioning is one of the most challenging tasks in AI, which aims to automatically generate textual sentences for an image.
Image Captioning with Context-Aware Auxiliary Guidance
Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image.
Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework
Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education.
A Relation-Specific Attention Network for Joint Entity and Relation Extraction
Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts.
Ranked #11 on
Relation Extraction
on WebNLG
Improving Abstractive Text Summarization with History Aggregation
Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization.
