Relation Networks for Object Detection (1711.11575v2)

Abstract: Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.

• The paper introduces an object relation module that leverages both appearance and geometric cues for improved object detection performance.
• The module integrates seamlessly into CNN architectures with minimal overhead, transforming duplicate removal into a learnable process.
• Experimental results show up to a +3.2 mAP gain, demonstrating its effectiveness across frameworks like Faster R-CNN and FPN.

Relation Networks for Object Detection

The paper "Relation Networks for Object Detection" by Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei, proposes a novel object relation module aimed at enhancing the object detection process in convolutional neural networks (CNNs). This module is designed to simultaneously process a set of objects by exploiting their appearance features and geometric relationships, thereby facilitating the modeling of object relations which has been a recognized, yet unexploited, approach in the deep learning era.

Key Contributions

1. Object Relation Module: The proposed module introduces a mechanism that leverages both appearance and geometric features of objects. This is operationalized via a redefined attention mechanism adapted specifically for object detection tasks. This redefinition incorporates relative geometric configurations of objects to ensure translation invariance, crucial for maintaining consistent performance across varying spatial arrangements of objects in images.
2. Implementation and Integration: The module is lightweight and in-place, implying that it can be seamlessly embedded into existing network architectures without requiring additional supervision or significant computational overhead. It integrates well with current region-based object detection pipelines by enhancing the instance recognition and duplicate removal steps.
3. Effectiveness and Versatility: The relation module improves the object detection performance across multiple state-of-the-art architectures such as Faster RCNN, Feature Pyramid Networks (FPN), and Deformable Convolutional Networks (DCN). Additionally, it transforms the traditional non-maximum suppression (NMS) post-processing step into a learnable task, thus contributing to the first fully end-to-end object detector.

Experimental Results

• Instance Recognition: By incorporating the relation module into the typical 2fc head structure of instance recognition, the performance showed marked improvement. For instance, incorporating relations after each fully connected layer with dimensions $1024$ achieved up to +3.2 mAP gain compared to the classic 2fc head alone. This indicates the module’s effectiveness in enabling joint reasoning of objects.
• Duplicate Removal: The paper also proposes a duplicate removal network which replaces the heuristic-based NMS. By utilizing object relations, the network refines its classification of correct detections versus duplicates. Comparative evaluations show that this approach not only surpasses the traditional NMS and the advanced SoftNMS but also offers adaptive thresholding for varying IoU criteria.

Implications and Future Directions

The empirical results underscore the importance of context and relational reasoning in object detection. This has theoretical implications for advancing visual understanding models, emphasizing the need to consider higher-order interactions beyond isolated object instances. Practically, the object relation module's ease of integration and parameter efficiency make it a viable enhancement for modern detection frameworks in various applications, from autonomous driving to surveillance and robotic vision.

Future developments might focus on extending these relation modules to other dense prediction tasks, such as semantic and instance segmentation, where spatial relationships among pixels and objects are equally critical. Additionally, exploring architectural modifications that may better capture long-range dependencies in dense object scenarios remains an open avenue, potentially necessitating innovations in relation modeling architectures akin to recent advancements in self-attention mechanisms in NLP.

Conclusion

The "Relation Networks for Object Detection" paper introduces a significant module that addresses the long-standing challenge of modeling object relations in deep learning. The proposed object relation module demonstrates consistent improvements across different detection architectures and tasks, offering both theoretical insights and practical enhancements. The research opens pathways for further exploration into relation-based models, heralding advancements in the contextual understanding required in advanced computer vision tasks.