Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects. In particular, a great deal of semantic information is carried in the relationships between objects. We have previously shown that the combination of a visual model and a statistical semantic prior model can improve on the task of mapping images to their associated scene description. In this paper, we review the model and compare it to a novel conditional multi-way model for visual relationship detection, which does not include an explicitly trained visual prior model. We also discuss potential relationships between the proposed methods and memory models of the human brain.
Visual relationship detection is concerned with the problem of detecting objects and their relationships in images. Extracting triples, i.e. visual relationships, from raw images is a challenging task, which has been a focus in the Semantic Web community for some time, e.g. @cite_0 @cite_11 and recently also gained substantial attention in mainstream computer vision @cite_4 @cite_8 @cite_20 . In the approach from @cite_8 , a Region Convolutional Neural Network (RCNN) for detecting and classifying objects in the image was used. Given a pair of objects, their relationship is predicted using another Convolutional Neural Network (CNN). The prediction is combined with a prior based on the word embeddings of the objects and the relationships. @cite_12 have used the same RCNN, but have replaced the word embedding model with a learned probabilistic semantic model. This model will be reviewed in Section . More recently, @cite_20 have applied the translational embedding model @cite_2 for the task of visual relationship detection.
@cite_4 @cite_8 @cite_0 @cite_2 @cite_12 @cite_20 @cite_11
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As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task. As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task. Reasoning about the relationships between object pairs in images is a crucial task for holistic scene understanding. Most of the existing works treat this task as a pure visual classification task: each type of relationship or phrase is classified as a relation category based on the extracted visual features. However, each kind of relationships has a wide variety of object combination and each pair of objects has diverse interactions. Obtaining sufficient training samples for all possible relationship categories is difficult and expensive. In this work, we propose a natural language guided framework to tackle this problem. We propose to use a generic bi-directional recurrent neural network to predict the semantic connection between the participating objects in the relationship from the aspect of natural language. The proposed simple method achieves the state-of-the-art on the Visual Relationship Detection (VRD) and Visual Genome datasets, especially when predicting unseen relationships (e.g. recall improved from 76.42 to 89.79 on VRD zero-shot testing set). The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. How to effectively integrate local and contextual visual cues from these regions has become a fundamental problem in object detection. Most existing works simply concatenated features or scores obtained from support regions. In this paper, we proposal a novel gated bi-directional CNN (GBD-Net) to pass messages between features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close iterations are modeled in a much more complex way. It is also shown that message passing is not always helpful depending on individual samples. Gated functions are further introduced to control message transmission and their on-and-off is controlled by extra visual evidence from the input sample. GBD-Net is implemented under the Fast RCNN detection framework. Its effectiveness is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.