Visual Relationship Detection
35 papers with code • 5 benchmarks • 5 datasets
Visual relationship detection (VRD) is one newly developed computer vision task aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition and is essential for fully understanding images, even the visual world.
The first, Entity Instance Confusion, occurs when the model confuses multiple instances of the same type of entity (e. g. multiple cups).
We use these benchmarks to study the performance of several state-of-the-art long-tail models on the LTVRR setup.
This requires the detection of visual relationships: triples (subject, relation, object) describing a semantic relation between a subject and an object.
One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks
Despite being widely used as a performance measure for visual detection tasks, Average Precision (AP) is limited in (i) reflecting localisation quality, (ii) interpretability and (iii) robustness to the design choices regarding its computation, and its applicability to outputs without confidence scores.
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).
This paper presents a framework for localization or grounding of phrases in images using a large collection of linguistic and visual cues.
To capture such global interdependency, we propose a deep Variation-structured Reinforcement Learning (VRL) framework to sequentially discover object relationships and attributes in the whole image.
The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent.
Generating scene graph to describe all the relations inside an image gains increasing interests these years.