Exploiting relationships between objects for image and video
captioning has received increasing attention. Most existing
methods depend heavily on pre-trained detectors of objects
and their relationships, and thus may not work well when
facing detection challenges such as heavy occlusion, tiny-size
objects, and long-tail classes. In this paper, we propose a joint
commonsense and relation reasoning method that exploits
prior knowledge for image and video captioning without relying on any detectors. The prior knowledge provides semantic
correlations and constraints between objects, serving as guidance to build semantic graphs that summarize object relationships, some of which cannot be directly perceived from images or videos. Particularly, our method is implemented by an
iterative learning algorithm that alternates between 1) commonsense reasoning for embedding visual regions into the
semantic space to build a semantic graph and 2) relation reasoning for encoding semantic graphs to generate sentences.
Experiments on several benchmark datasets validate the effectiveness of our prior knowledge-based approach.
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Attentive Relational Networks for Mapping Images to Scene Graphs
Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and
their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention
module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced
by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and
the results demonstrate the effectiveness and superiority of our model.
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- NSF-PAR ID:
- 10103951
- Date Published:
- Journal Name:
- Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
