Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships (1807.00119v1)

Abstract: Context is important for accurate visual recognition. In this work we propose an object detection algorithm that not only considers object visual appearance, but also makes use of two kinds of context including scene contextual information and object relationships within a single image. Therefore, object detection is regarded as both a cognition problem and a reasoning problem when leveraging these structured information. Specifically, this paper formulates object detection as a problem of graph structure inference, where given an image the objects are treated as nodes in a graph and relationships between the objects are modeled as edges in such graph. To this end, we present a so-called Structure Inference Network (SIN), a detector that incorporates into a typical detection framework (e.g. Faster R-CNN) with a graphical model which aims to infer object state. Comprehensive experiments on PASCAL VOC and MS COCO datasets indicate that scene context and object relationships truly improve the performance of object detection with more desirable and reasonable outputs.

Structure Inference Network: Enhancing Object Detection with Contextual Information

The paper "Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships" presents an advanced approach to object detection by incorporating both scene-level and instance-level contextual information into a deep learning framework. The methodology advances conventional object detectors by regarding object detection as an integrated cognition and reasoning task through the use of structured information. The proposed Structure Inference Network (SIN) is introduced as a means to blend graph-based inference with typical object detection models, notably Faster R-CNN, enabling improved detection performance through scene context and object relationships.

The paper acknowledges the current limitations of leading object detection methods, which often rely heavily on visual signals localized within the boundary boxes. Such approaches may be susceptible to errors where the contextual information of objects and scenes is not leveraged. The paper proposes a unique solution by formulating object detection as a graph structure inference problem. In this approach, objects are nodes within a graph and the relationships between objects are defined as edges.

The SIN model utilizes graphical modeling techniques to explore the structured relationships among objects within an image. The method initiates with the identification of region proposals that are mapped onto nodes in a graphical structure. Scene information is extracted as a separate node to influence the overall graph. Two core concepts drive the model: the use of scene-level context and instance-level graphical relationships.

Core Methodology and Innovations

• Graphical Modeling: SIN reformulates object detection into a graph framework where each object is a node, and the edges represent potential object-object relationships.
• Scene and Edge GRUs: Utilizing Gated Recurrent Units (GRUs), the paper introduces discrete modules to process scene context and integrate it with relational information. The scene GRUs influence each node based on the overall scene, while edge GRUs iteratively update the node states using object-object relationships.
• Message Aggregation: The paper leverages a combination of scene-derived and relationship-informed messages to iteratively refine each node's representation, enhancing the model's overall accuracy in object detection tasks.

Experimental Results

The experimental validation of SIN on datasets like PASCAL VOC and MS COCO demonstrate its efficacy over baseline models such as Faster R-CNN. Noteworthy findings include:

• Improved mAP: The model achieves an mAP of 76.0% on VOC 2007 test and 23.2% on COCO 2015 test-dev, surpassing typical detection algorithms.
• Small Objects and Localization: Enhanced precision in the detection of small or overlapped objects is attributed to the use of contextual information, which is imperative in real-world scenarios where such objects are frequently observed.
• Effectiveness of Contextual Information: Detailed analysis reveals significant improvements in categories like aeroplane and boat, which traditionally benefit from scene context for correct identification.

Theoretical and Practical Implications

The integration of scene and relational context holds promise for improving the robustness and versatility of object detection frameworks. The model shows potential for immediate application in various computer vision tasks, from autonomous driving to surveillance systems where contextual cues are valuable. It paves the way for future frameworks where semantic understanding and reasoning are integrated more deeply into detection tasks.

Future Directions

Further exploration into optimizing message passing mechanisms could refine the balance between context and rare-event detection. This aligns with the growing interest in enhancing detection accuracy in dynamically complex and diverse environments. The inherent flexibility of SIN opens avenues for broader applications, including more sophisticated scene understanding and interactive environments.

In conclusion, the Structure Inference Network significantly diversifies the representational capabilities of object detection models by incorporating two distinct but complementary forms of contextual data. It represents an incremental yet powerful refinement in how detection models can understand and interpret visual information, holding potential for scalable improvements across a variety of tasks and datasets.