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Found 6 papers, 3 papers with code
RelVAE: Generative Pretraining for few-shot Visual Relationship Detection
Visual relations are complex, multimodal concepts that play an important role in the way humans perceive the world.
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction
We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD).
Interpretable Visual Question Answering via Reasoning Supervision
Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task.
An Incremental Learning framework for Large-scale CTR Prediction
In this work we introduce an incremental learning framework for Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for Taboola's massive-scale recommendation service.
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection
Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Detecting visual relationships, i. e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch.
