Multi-scale Location-aware Kernel Representation for Object Detection (1804.00428v1)

Abstract: Although Faster R-CNN and its variants have shown promising performance in object detection, they only exploit simple first-order representation of object proposals for final classification and regression. Recent classification methods demonstrate that the integration of high-order statistics into deep convolutional neural networks can achieve impressive improvement, but their goal is to model whole images by discarding location information so that they cannot be directly adopted to object detection. In this paper, we make an attempt to exploit high-order statistics in object detection, aiming at generating more discriminative representations for proposals to enhance the performance of detectors. To this end, we propose a novel Multi-scale Location-aware Kernel Representation (MLKP) to capture high-order statistics of deep features in proposals. Our MLKP can be efficiently computed on a modified multi-scale feature map using a low-dimensional polynomial kernel approximation.Moreover, different from existing orderless global representations based on high-order statistics, our proposed MLKP is location retentive and sensitive so that it can be flexibly adopted to object detection. Through integrating into Faster R-CNN schema, the proposed MLKP achieves very competitive performance with state-of-the-art methods, and improves Faster R-CNN by 4.9% (mAP), 4.7% (mAP) and 5.0% (AP at IOU=[0.5:0.05:0.95]) on PASCAL VOC 2007, VOC 2012 and MS COCO benchmarks, respectively. Code is available at: https://github.com/Hwang64/MLKP.

• The paper’s main contribution is the MLKP, which integrates high-order statistics to improve object detection performance by up to 5% mAP.
• The method efficiently computes low-dimensional polynomial kernels using 1x1 convolutions and retains spatial information via a location-weight network.
• Experiments on PASCAL VOC and MS COCO datasets validate the approach, demonstrating robust gains across multiple benchmarks.

Multi-scale Location-aware Kernel Representation for Object Detection

The paper "Multi-scale Location-aware Kernel Representation for Object Detection" presents a novel approach that aims to enhance the performance of object detection frameworks, particularly Faster R-CNN, by leveraging high-order statistical representations. Faster R-CNN and its variants traditionally rely on first-order feature representations, which, while effective, may not capture the full complexity of the data, especially in challenging contexts where precise localization and discrimination are required.

Technical Contributions

1. High-Order Statistics Integration: The core contribution of the paper is the introduction of the Multi-scale Location-aware Kernel Representation (MLKP), which incorporates high-order statistics into the object proposal representations used by detection frameworks. The authors argue that such statistics can provide a more discriminative representation, thereby improving detection accuracy.
2. Efficient Computation: Recognizing the computational and memory constraints of incorporating high-order statistics, the authors propose using a low-dimensional polynomial kernel approximation to efficiently compute these representations. This is achieved by reformulating polynomial kernel representations as $1 \times 1$ convolution operations followed by element-wise products.
3. Location Retention: A significant innovation of the MLKP is its ability to retain location information—a crucial aspect for object detection tasks. This is accomplished through a location-weight network that adjusts the contribution of different spatial regions dynamically, based on their relevance to the object detection task.
4. Multi-Scale Feature Maps: Another salient aspect of the proposed method is its utilization of multi-scale feature maps. The authors enhance standard practice by modifying the strategy to include features from multiple layers within the convolutional blocks, ensuring that information from various resolutions is effectively harnessed, particularly beneficial for detecting small objects.

Experimental Results

The proposed MLKP is benchmarked against traditional methods across widely recognized datasets, including PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO. The empirical results underscore meaningful improvements in performance metrics, notably:

• A 4.9% increase in mean Average Precision (mAP) on PASCAL VOC 2007.
• Gains of 4.7% and 5.0% on VOC 2012 and MS COCO benchmarks, respectively, demonstrating its robustness across different datasets.

Implications and Future Directions

The integration of high-order statistics within the object detection pipeline provides compelling evidence for its utility, suggesting that similar approaches could be developed for other tasks within computer vision where detail and precision are paramount. While computational efficiency remains a challenge, the method's ability to offer enhanced performance with manageable overheads is promising.

For future developments, the authors hint at potential expansions, such as integrating the MLKP into region-free detection methods like YOLO and SSD. However, the direct application might require addressing the absence of explicit region proposals in these methods.

Overall, the paper showcases a method that not only competes with but often surpasses state-of-the-art techniques by rethinking the statistical underpinnings of object representations in detection frameworks. It sets a trajectory for further research into how complex statistical models can be made tractable and beneficial in practical applications.