Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation (1707.09423v2)

Abstract: Understanding visual relationships involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the (subj,obj) pair (both semantically and spatially) to predict the predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships, but complicates learning since the semantic space of visual relationships is huge and the training data is limited, especially for the long-tail relationships that have few instances. To overcome this, we use knowledge of linguistic statistics to regularize visual model learning. We obtain linguistic knowledge by mining from both training annotations (internal knowledge) and publicly available text, e.g., Wikipedia (external knowledge), computing the conditional probability distribution of a predicate given a (subj,obj) pair. Then, we distill the knowledge into a deep model to achieve better generalization. Our experimental results on the Visual Relationship Detection (VRD) and Visual Genome datasets suggest that with this linguistic knowledge distillation, our model outperforms the state-of-the-art methods significantly, especially when predicting unseen relationships (e.g., recall improved from 8.45% to 19.17% on VRD zero-shot testing set).

• The paper introduces a knowledge distillation framework that fuses internal annotations and external linguistic data to overcome the semantic scarcity in visual relationship detection.
• It employs a teacher-student model integrating semantic, spatial, and visual features, achieving a recall boost from 8.45% to 19.17% on zero-shot testing set for unseen relationships.
• The method enhances model generalization for rare relationships, improving scene understanding in applications such as autonomous driving and advanced image annotation.

Overview of Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation

The paper in discussion, authored by Ruichi Yu, Ang Li, Vlad I. Morariu, and Larry S. Davis, addresses the challenging problem of visual relationship detection in images. This task involves identifying tuples consisting of a subject, an object, and a predicate that describes the interaction between them. The primary contribution of the paper is the introduction of a method that leverages both internal and external linguistic knowledge to enhance the learning capabilities of deep neural networks in predicting these relationships.

The authors point out a significant limitation in existing approaches: the expansive semantic space of possible visual relationships and the scarcity of training examples, particularly those involving long-tail relationships with few instances. To mitigate this, they propose a novel knowledge distillation framework that integrates linguistic statistical information drawn from annotated training data (internal knowledge) as well as publicly available corpuses such as Wikipedia (external knowledge). This knowledge distillation strategy aims to regularize the learning process of the deep neural network, improving its generalization capabilities, especially on unseen or rare visual relationships.

The paper evaluates the performance of this framework primarily on Visual Relationship Detection (VRD) and Visual Genome datasets, highlighting noteworthy improvements. For instance, the method achieves a recall increase from 8.45% to 19.17% when predicting unseen relationships on the VRD zero-shot testing set.

Technical Contributions

The technical backbone of the research lies in the fusion of semantic, spatial, and visual features to train a sophisticated end-to-end neural network model. The exploitation of word embeddings for semantic representation of objects, coupled with spatial features encoding the relative positions of objects, enhances the model's capability to understand nuanced visual relationships.

The authors employ a teacher-student framework for knowledge distillation. Here, the student network assimilates ground truth labels as well as soft constraints imposed by linguistic knowledge. This dual learning process allows the model to become more resilient to overfitting and more adept in generalizing relationships outside the training data distribution.

The incorporation of internal linguistic knowledge from the training annotations is an effective strategy. However, the enrichment via external knowledge sources such as Wikipedia provides the model with additional context, thereby addressing data insufficiencies and enhancing zero-shot learning capabilities.

Experimental Results

Empirical evaluations using standard metrics reflect the model's improved performance over existing methods like those proposed by Lu et al. (VRD) and newer approaches utilizing deep learning and logical knowledge frameworks. The research observes superior results not only for visual relationship detection but also in related tasks such as phrase detection and relationship detection, further validating the robustness of the proposed method.

Implications and Future Directions

The work has significant implications in the broader field of computer vision and AI. By effectively bridging the gap between visual data and linguistic understanding, the approach offers a robust pathway to improve relationship detection algorithms, which are crucial for advanced scene understanding needed in applications like autonomous driving, robotics, and complex image annotation tasks.

Looking forward, potential avenues for research include refining the extraction and integration of external linguistic knowledge, perhaps employing more precise LLMs to reduce noise from non-visual domains. Furthermore, the expansion of dataset sizes and diversity, coupled with advancements in natural language processing, could further improve the effectiveness of the knowledge distillation framework, particularly in dealing with the long-tail distribution of visual relationships. Continuing exploration into multi-modal frameworks that combine vision and language holds the promise of achieving more comprehensive understanding and reasoning in AI systems.