LSTD: A Low-Shot Transfer Detector for Object Detection (1803.01529v1)

Abstract: Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate transfer difficulties in low-shot detection. This architecture can integrate the advantages of both SSD and Faster RCNN in a unified deep framework. Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images. Finally, we examine our LSTD on a number of challenging low-shot detection experiments, where LSTD outperforms other state-of-the-art approaches. The results demonstrate that LSTD is a preferable deep detector for low-shot scenarios.

• The paper presents LSTD, a Low-Shot Transfer Detector framework designed for object detection when only limited training data is available.
• LSTD utilizes a novel architecture combining SSD and Faster R-CNN features with Transfer Knowledge and Background Depression regularizations to handle scarce data.
• Empirical results show LSTD outperforms existing methods on benchmark datasets, providing a practical solution for real-world object detection with minimal annotations.

A Thorough Analysis of the Low-Shot Transfer Detector (LSTD) for Object Detection

The paper "LSTD: A Low-Shot Transfer Detector for Object Detection," authored by Hao Chen et al., presents a solution to the challenge of object detection with limited annotated training data. Traditional object detection models often rely on ample labeled datasets to perform accurately. However, in many practical applications, obtaining such comprehensive data is not feasible. This paper proposes the Low-Shot Transfer Detector (LSTD), which addresses this limitation by leveraging knowledge from a source domain with ample data to improve detection in a target domain with scarce data.

Key Contributions

1. Innovative Architecture Design: The LSTD framework integrates the favorable attributes of the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (Faster RCNN). This integration occurs within a unified architecture to enhance low-shot detection. The multi-convolutional-layer design of SSD supports bounding box regression, while the coarse-to-fine methodology of Faster RCNN assists in object classification, making the architecture well-suited for scenarios with limited data.
2. Regularized Transfer Learning Framework: This framework mitigates the task differences between source and target domains by introducing Transfer Knowledge (TK) and Background Depression (BD) regularizations. These regularizations help mitigate overfitting and improve model generalization by using limited annotated examples to suppress background interference and integrate source-domain object knowledge delicately.
3. Performance Evaluation: Empirical results indicate that LSTD surpasses existing low-shot detection approaches, particularly those based on weakly or semi-supervised learning. The model exhibits superior performance in traditional object detection scenarios by demonstrating minimal reliance on large datasets.

Numerical Results and Claims

The paper presents rigorous experimentation on benchmark datasets, including COCO, ImageNet2015, and PASCAL VOC, structured to evaluate both source and target domains across different tasks. The LSTD framework consistently demonstrates enhanced performance and robustness, especially as the number of training examples in the target domain increases. For instance, in Task 1, the LSTD achieves mAP scores that significantly exceed those of competing frameworks, including the SSD and Faster RCNN methodologies, especially notable in low-shot contexts with only a few examples per category.

Practical and Theoretical Implications

Practically, LSTD provides a viable solution for object detection in real-world settings where only sparse annotations are available, reducing costs and logistical burdens associated with data collection and annotation. Theoretically, the approach contributes to the field of transfer learning by offering a structured way to incorporate knowledge across domains with varying amounts of data.

Future Directions

The development of LSTD opens several avenues for future research. Enhancements could further improve the efficiency of the transfer learning process, particularly in optimizing the balance between transfer learning from the source domain and learning from the limited data in the target domain. Additionally, exploring other deep learning architectures that may integrate effectively with LSTD could yield beneficial results, potentially enhancing its capabilities in even more restrictive low-shot settings.

In conclusion, the LSTD framework represents a significant stride in the domain of object detection under constrained data conditions, providing both theoretical insights and practical enhancements to how detection systems can be trained and deployed efficiently. It underscores the potential of transfer learning as a practical tool, bridging the knowledge gap between richly annotated datasets and real-world scenarios with limited data availability.