Relation Network
54 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Relation Network
Most implemented papers
Learning to Compare: Relation Network for Few-Shot Learning
Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.
AVA-AVD: Audio-Visual Speaker Diarization in the Wild
Audio-visual speaker diarization aims at detecting "who spoke when" using both auditory and visual signals.
Temporal Relational Reasoning in Videos
Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species.
Temporal Relational Ranking for Stock Prediction
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
Local Relation Networks for Image Recognition
The convolution layer has been the dominant feature extractor in computer vision for years.
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
Existing work on augmenting question answering (QA) models with external knowledge (e. g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Semantic parsing has long been a fundamental problem in natural language processing.