Search Results for author:
Found 9 papers, 5 papers with code
EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs.
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning
To this end, we propose CoLE, a Co-distillation Learning method for KG Embedding that exploits the complementarity of graph structures and text information.
Learning to Answer Questions in Dynamic Audio-Visual Scenarios
In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to answer questions regarding different visual objects, sounds, and their associations in videos.
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Rule-Guided Graph Neural Networks for Recommender Systems
However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items.
Deep Inception Generative Network for Cognitive Image Inpainting
Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting.
Scene Text Detection with Supervised Pyramid Context Network
We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.
Ranked #2 on
Scene Text Detection
on ICDAR 2013
Real-world plant species identification based on deep convolutional neural networks and visual attention
We propose a novel framework and an effective data augmentation method for deep learning in this paper.
