ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization (1609.04331v1)

Abstract: We aim to localize objects in images using image-level supervision only. Previous approaches to this problem mainly focus on discriminative object regions and often fail to locate precise object boundaries. We address this problem by introducing two types of context-aware guidance models, additive and contrastive models, that leverage their surrounding context regions to improve localization. The additive model encourages the predicted object region to be supported by its surrounding context region. The contrastive model encourages the predicted object region to be outstanding from its surrounding context region. Our approach benefits from the recent success of convolutional neural networks for object recognition and extends Fast R-CNN to weakly supervised object localization. Extensive experimental evaluation on the PASCAL VOC 2007 and 2012 benchmarks shows hat our context-aware approach significantly improves weakly supervised localization and detection.

• The paper introduces context-aware additive and contrastive models that significantly improve weakly supervised object localization.
• The additive model enhances semantic association by summing ROI and context scores, while the contrastive model differentiates objects from background.
• Experimental results on PASCAL VOC datasets show notable gains in mAP and CorLoc, underscoring the practical impact of integrating contextual information.

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

The paper "ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization" by Vadim Kantorov et al. introduces novel context-aware models designed to enhance the efficacy of weakly supervised object localization (WSL) using convolutional neural networks (CNNs). The motivation behind the proposed approach arises from the challenging nature of WSL, which relies solely on image-level annotations, foregoing the fine-grained annotations such as object bounding boxes typically used in strongly supervised methods. The significant contribution of this work is the introduction of context-aware guidance, incorporated through additive and contrastive models, which improve localization accuracy by integrating context regions around the object candidate regions.

Context-Aware Models

The primary innovation presented in this work is the modeling of context-aware guidance through two distinct strategies:

1. Additive Model: This model leverages context by ensuring that the predicted object region is semantically compatible with its surrounding context. It achieves this by maximizing the sum of class scores derived from both the region of interest (ROI) and its context.
2. Contrastive Model: The contrastive model aims to enhance object detection by ensuring the object region stands out from its background. This is realized by maximizing the difference between the class score of the candidate ROI and that of the context.

Both models extend the Fast R-CNN architecture to weakly supervised settings, where CNNs are tailored to incorporate contextual information for improved localization precision.

Experimental Evaluation

The proposed models, additive and contrastive, were evaluated on standard WSL benchmarks, specifically the PASCAL VOC 2007 and 2012 datasets. The experiments reveal that the context-aware models substantially outperform baseline methods by integrating contextual information during training. Notably, the contrastive model with symmetric feature pooling (denoted as Contrastive S) achieved the highest performance, significantly enhancing both detection mean average precision (mAP) and Correct Localization (CorLoc) metrics over baseline methods.

Insights and Implications

The extensive experimental results underscore the benefits of incorporating contextual information into CNNs for object localization. Notably, the contrastive approach addresses a common weakness in WSL, where detections tend to shrink to highly discriminative parts rather than encapsulate full object extents. The reported findings suggest that context, even in a weakly supervised regime, holds substantial potential to guide localization processes, thereby reducing reliance on extensive manual annotations.

The paper paves the way for further investigation into context-aware weakly supervised learning, suggesting that future research could explore alternative context modeling methods or integrate this approach with other evolutive models for greater enhancements in localization tasks. Additionally, while the paper primarily addresses WSL in object localization, the underlying principles could extend to other computer vision tasks where contextual cues play a pivotal role.

In conclusion, this research provides a significant step forward in refining the capabilities of weakly supervised learning in object localization. By smartly leveraging context information, it offers a resource-efficient approach that reduces the dependence on cumbersome annotation datasets while still achieving robust localization performance.