Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects.
Despite their effectiveness, however, current SGG methods only assume scene graph homophily while ignoring heterophily.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
Moreover, since the backbones are query-agnostic, it is difficult to completely avoid the inconsistency issue by training the visual backbone end-to-end in the visual grounding framework.
A common problem in both pruning and distillation is to determine compressed architecture, i. e., the exact number of filters per layer and layer configuration, in order to preserve most of the original model capacity.
This is very helpful for the decision makers, especially when facing changing environments.
To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies.
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
As a result, this model performs quite well in both validation and explanation.
There are three key properties of scene graph that have been underexplored in recent works: namely, the edge direction information, the difference in priority between nodes, and the long-tailed distribution of relationships.
Ranked #3 on Scene Graph Generation on Visual Genome
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.