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Found 7 papers, 3 papers with code
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy
The recently proposed Limit-based Scoring Loss independently limits the range of positive and negative triplet scores.
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation
By using previous knowledge graph embedding methods, every entity in a knowledge graph is usually represented as a k-dimensional vector.
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations
It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry.
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.
Transformer Based Line Segment Classifier With Image Context for Real-Time Vanishing Point Detection in Manhattan World
To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points.
Multi-Target Domain Adaptation with Collaborative Consistency Learning
To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.
Ranked #4 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed.
