Attentive Contexts for Object Detection (1603.07415v1)

Abstract: Modern deep neural network based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. In this work, we take a step towards understanding what is a robust practice to extract and utilize contextual information to facilitate object detection in practice. Specifically, we consider the following two questions: "how to identify useful global contextual information for detecting a certain object?" and "how to exploit local context surrounding a proposal for better inferring its contents?". We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model. AC-CNN effectively incorporates global and local contextual information into the region-based CNN (e.g. Fast RCNN) detection model and provides better object detection performance. It consists of one attention-based global contextualized (AGC) sub-network and one multi-scale local contextualized (MLC) sub-network. To capture global context, the AGC sub-network recurrently generates an attention map for an input image to highlight useful global contextual locations, through multiple stacked Long Short-Term Memory (LSTM) layers. For capturing surrounding local context, the MLC sub-network exploits both the inside and outside contextual information of each specific proposal at multiple scales. The global and local context are then fused together for making the final decision for detection. Extensive experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of the proposed AC-CNN over well-established baselines. In particular, AC-CNN outperforms the popular Fast-RCNN by 2.0% and 2.2% on VOC 2007 and VOC 2012 in terms of mAP, respectively.

An Overview of "Attentive Contexts for Object Detection"

The paper "Attentive Contexts for Object Detection" presents a novel approach to object detection within the deep learning framework, utilizing a contextually aware convolutional neural network (CNN) model known as the Attention to Context Convolutional Neural Network (AC-CNN). The paper addresses the limitations of traditional CNN-based object detectors which predominantly focus on the features within individual object proposals and overlook potentially valuable contextual information, both on global and local levels. This research provides preliminary answers to enhancing object detection by examining how to optimally utilize contextual data surrounding a proposal.

Methodology and Model Architecture

The AC-CNN model is designed to be an extension of the conventional region-based CNN detectors, such as Fast R-CNN, by integrating both global and local contextual information. The AC-CNN comprises two key components:

1. Attention-Based Global Contextualized Sub-network: This sub-network employs a recurrent neural network, specifically Long Short-Term Memory (LSTM) layers, to create an attention mechanism that identifies globally relevant contextual information across an entire image. The method includes constructing an attention map to prioritize regions with meaningful global context, effectively filtering out extraneous information that might negatively impact detection accuracy.
2. Multi-Scale Local Contextualized Sub-network: For a more granular inspection, this sub-network considers local contexts by pooling features across multiple scales within and around a specific object proposal. The approach ensures inside and outside boundaries of proposals are evaluated, potentially revealing discriminative characteristics useful for precise detection.

Experimental Validation

The effectiveness of the AC-CNN was validated through experiments conducted on the PASCAL VOC 2007 and VOC 2012 datasets, demonstrating a performance increase of 2.0% and 2.2% in mean Average Precision (mAP) over the well-established Fast-RCNN model. The research outlines marked improvements in the detection of challenging object classes, notably small or occluded objects, which typically present difficulties with traditional methods.

Implications and Future Research Directions

The incorporation of both global and local contextual information in AC-CNN shows promising implications for enhancing object detection accuracy. Highlighting the importance of context, the paper suggests that attention mechanisms can significantly aid in filtering critical feature regions, thus improving decision-making in object classification tasks.

For future research, there could be interest in further refining attention mechanisms to minimize computational costs while maximizing detection accuracy. Additionally, expanding the application of such contextually aware models to other vision tasks beyond object detection, such as semantic segmentation or instance detection, could provide a wide spectrum of benefits.

Overall, this paper contributes valuable insights into the domain of object detection by elucidating the potential of contextual information and novel network architectures to overcome existing limitations. The findings pave the way for continued exploration into more sophisticated and context-sensitive neural network designs within computer vision.