Single-Shot Object Detection with Enriched Semantics (1712.00433v2)

Abstract: We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a global activation module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.7 on VOC2007 test and an mAP of 32.8 on COCO test-dev with an inference speed of 31.5 milliseconds per image on a Titan Xp GPU. With a lower resolution version, we achieve an mAP of 79.7 on VOC2007 with an inference speed of 13.0 milliseconds per image.

• The paper presents the DES network that enriches feature semantics by incorporating a segmentation branch and global activation modules.
• It achieves significant performance gains with an mAP of 81.7 on VOC2007 and 32.8 on COCO using a VGG16 backbone.
• The approach is computationally efficient, processing images in 31.5 ms on a Titan Xp GPU, making it suitable for real-time applications.

Single-Shot Object Detection with Enriched Semantics: An Overview

The paper "Single-Shot Object Detection with Enriched Semantics" introduces an innovative approach to enhance the semantic understanding of features within single-shot object detection frameworks. The authors propose the Detection with Enriched Semantics (DES) network, which integrates a novel semantic segmentation branch and a global activation module into existing deep detection architectures, specifically aiming to enrich feature semantics without the need for additional annotations.

Key Contributions

1. Segmentation Branch: The DES network includes a segmentation branch which utilizes weak segmentation ground-truth to improve the semantic richness of low-level features. By integrating this branch, the network can augment initial detection layers, thus allowing it to better capture both basic visual patterns and high-level semantic information.
2. Global Activation Module: To enhance the higher-level detection features, the paper introduces global activation blocks. These blocks are designed to learn channel-object class relationships via self-supervised methods, maintaining channel integrity while augmenting semantic capacity.
3. Performance Improvement: Through comprehensive experiments conducted on PASCAL VOC and MS COCO datasets, the authors demonstrate significant improvements in mean Average Precision (mAP) over traditional SSD models. Using VGG16 as the backbone, DES achieves an mAP of 81.7 on VOC2007 and 32.8 on COCO test-dev, substantiating the performance gains both in precision and speed compared to existing methodologies.
4. Efficiency: DES is shown to be computationally efficient. With an inference speed of 31.5 milliseconds per image on a Titan Xp GPU, DES stands competitive against alternative methods, offering an optimal balance between detection accuracy and processing speed.

Implications and Future Directions

The proposed methodology underlines an important stride in single-shot object detection by demonstrating how auxiliary tasks, specifically semantic segmentation, can be leveraged to substantially boost object detection performance. The use of a weakly supervised segmentation branch is of particular interest, as it does not require additional datasets or ground-truths beyond bounding boxes, making the approach scalable and versatile.

Practically, this enriched semantic understanding could be employed in various applications, from autonomous vehicles to surveillance systems where quick, accurate identification of objects is critical. Moreover, the proposed global activation blocks open avenues for further research on channel-object class relationship leveraging self-supervised learning techniques.

A plausible future research direction involves integrating the DES framework with more advanced backbones such as ResNet or newer architectures like EfficientNet, potentially offering even greater advancements in both efficiency and precision. Furthermore, exploring cross-disciplinary applications where enriched semantic information could play a crucial role remains an inviting prospect for future work in Artificial Intelligence and Computer Vision.

In conclusion, the DES framework exemplifies a promising step forward in enhancing object detection frameworks by merging complementary semantic tasks, foregrounding the notion that deeper, richer semantic understanding can be achieved through clever architecture augmentation without imposing extensive computational overheads.