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Attentive Relational Networks for Mapping Images to Scene Graphs
Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo
TL;DR
Scene graph generation must infer both object identities and their contextual relationships, which conventional object detectors do not capture. The Attentive Relational Network addresses this with semantic transformation and graph self-attention modules, and experiments on Visual Genome report superior performance over state-of-the-art methods.
Problem
Scene graph generation remains difficult because conventional object detectors cannot infer image relationships, while prior methods often neglect visual-linguistic semantics and global graph structure.
Method
The Attentive Relational Network uses object detection, semantic transformation into a common visual-linguistic space, graph self-attention over neighboring nodes, and relation inference.
Results
The method outperforms state-of-the-art scene graph generation methods on the Visual Genome Dataset.
Takeaways & Limitations
The reported experiments demonstrate the effectiveness of combining semantic transformation with joint graph representation embedding for scene graph generation.
Takeaways & Limitations
Qualitative errors arise from relation ambiguity and object-detection failures, and the authors note that advanced object detection could improve performance.
Abstract
from arXiv · showhide
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.
1. Introduction
Scene graph generation maps images to structured representations of entities and their relationships, addressing contextual reasoning that conventional object detectors do not capture. The proposed Attentive Relational Network combines semantic transformation and graph self-attention modules, with experiments on Visual Genome showing superior performance.
- Motivation: Scene graph generation captures object entities and pair-wise relationships within an image.It supports applications including image retrieval, captioning, visual question answering, and video analysis.
- Motivation: Conventional object detection improves recognition but cannot capture and infer relationships within images.Scene graph generation remains challenging because visual appearances and relations are highly diverse.
- Limitations of Prior Work: Existing methods often neglect visual-linguistic semantics and intra-triplet connections when modeling subject-predicate-object relationships.They cover spatial, attribute, comparative, and action relations.
- Limitations of Prior Work: Prior CNN- and RNN-based approaches commonly require known graph structures, use intensive matrix operations, process nodes and edges step by step, and neglect global scene information.Joint graph representation for whole-scene reasoning therefore remains difficult.
- Proposed Method: The Attentive Relational Network uses object detection, semantic transformation, graph self-attention, and relation inference to produce scene graphs.Semantic transformation maps visual and linguistic features into a common space, while graph self-attention weights neighboring nodes.
- Experiments: Extensive Visual Genome experiments verify superior performance compared with state-of-the-art methods.The ablation study also reports that the semantic transformation module improves scene graph generation.
2. Related Work
Prior scene graph and visual relationship detection research includes RNN-, CNN-, embedding-, reinforcement-learning-, and proposal-based approaches. These methods variously model message passing, motifs, permutation invariance, dynamic graphs, or pairwise relations, but often fail to represent the whole scene jointly.
- Scene Graph Generation: Scene graph generation methods include RNN-based message passing, motif modeling, permutation-invariant prediction, dynamic graphs, and CNN-based associative embeddings.The literature is commonly divided into recurrent and convolutional approaches.
- Visual Relationship Detection: Visual relationship detection methods model triplets, visual embeddings, grounded phrases, reinforcement-learning sequences, statistical dependencies, or pairwise relationship proposals.These approaches use varied visual, linguistic, and structural cues.
- Visual Relationship Detection: Many visual relationship detection methods detect relationships one by one and cannot learn the graph structure describing positional and logical relationships between objects.This limits their suitability for representing the structure of an entire scene.
3. Proposed Approach
The Attentive Relational Network combines object detection, semantic transformation, graph self-attention, and relation inference to produce scene graphs. It integrates visual and linguistic representations while weighting neighboring nodes according to graph structure.
- Relation Inference Module: The architecture forms a joint global graph representation from semantic relation and entity embeddings, then predicts entity and relation labels.The final relation inference stage recognizes entities and relationships using multilayer perceptrons and cross-entropy losses.
- Overview: The model detects entity and relation features, locations, and initial classification scores before subsequent graph reasoning.It uses Faster R-CNN, union boxes for object pairs, appearance and spatial features, and softmax classification.
- Semantic Transformation Module: Semantic transformation maps visual features and label word embeddings into a common relation space for representing subject-predicate-object relations.The module concatenates entity, relation, and label features and applies three learnable weight matrices.
- Graph Self-Attention Module: Graph self-attention embeds nodes using neighboring features and learned importance weights derived from an adjacency structure based on spatial clues.Neighbor coefficients are normalized with softmax, and multi-head attention concatenates features from K independent attention mechanisms.
4. Experimental Results
Experiments on Visual Genome evaluate the proposed network on SGCls and PredCls, comparing its modules, variants, and qualitative behavior. The full model surpasses prior methods, while ablations show distinct contributions from semantic transformation and graph self-attention.
- Experimental Settings: Evaluation uses Visual Genome with Recall@50 and Recall@100 for scene graph generation, focusing on SGCls and PredCls under fixed task settings.SGCls predicts entity and relation categories from ground-truth boxes; PredCls predicts relations from ground-truth boxes and categories.
- Quantitative Comparisons: Table 1 compares the full model and module variants against Language Prior, IMP, Neural Motif, Graph R-CNN, and GPI.The table reports constrained SGCls and PredCls results on the Visual Genome test set.
- Quantitative Comparisons: 38.2% and 40.4% are the full model’s SGCls Recall@50 and Recall@100, approximately 2% above GPI on both metrics.The reported gains concern the strong GPI baseline and indicate improved relation capture between entity pairs.
- Ablation Study: The graph self-attention variant improves substantially over the baseline and exceeds Neural Motif and Graph R-CNN by 2% and 8%, respectively.Weighted neighbor embeddings let nodes focus on neighboring features according to contextual relations.
- Ablation Study: The semantic transformation-only variant outperforms state-of-the-art methods and other model variants, while concatenation with semantic loss performs best among fusion alternatives.PCA visualization shows object, predicate, and subject clusters alongside entity-relation affinities in a shared semantic space.
- Qualitative Results: Qualitative results show correct relation and subject-object direction predictions, but errors remain from predicate ambiguity and detector failures.The authors identify inaccurate entities and missing relations as detector-related failures and note that stronger detection could help.
5. Conclusion
The paper presents an Attentive Relational Network for scene graph generation and reports superior performance on Visual Genome. Its two modules project visual and linguistic information jointly and embed graph structure through attention.
- Conclusion: The Attentive Relational Network maps images to scene graphs using semantic transformation and graph self-attention modules.The paper evaluates the method on the Visual Genome Dataset.
- Conclusion: The semantic transformation module projects visual features and linguistic knowledge into a common space, while graph self-attention embeds a joint graph representation.These modules are presented as the paper’s central architectural contributions.
- Conclusion: Experiments on Visual Genome report performance superior to state-of-the-art methods for scene graph generation.The conclusion attributes this result to the effectiveness of the proposed model.