Attention-guided Context Feature Pyramid Network for Object Detection (2005.11475v1)

Abstract: For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question. In this paper, to tackle this issue, we build a novel architecture, called Attention-guided Context Feature Pyramid Network (AC-FPN), that exploits discriminative information from various large receptive fields via integrating attention-guided multi-path features. The model contains two modules. The first one is Context Extraction Module (CEM) that explores large contextual information from multiple receptive fields. As redundant contextual relations may mislead localization and recognition, we also design the second module named Attention-guided Module (AM), which can adaptively capture the salient dependencies over objects by using the attention mechanism. AM consists of two sub-modules, i.e., Context Attention Module (CxAM) and Content Attention Module (CnAM), which focus on capturing discriminative semantics and locating precise positions, respectively. Most importantly, our AC-FPN can be readily plugged into existing FPN-based models. Extensive experiments on object detection and instance segmentation show that existing models with our proposed CEM and AM significantly surpass their counterparts without them, and our model successfully obtains state-of-the-art results. We have released the source code at https://github.com/Caojunxu/AC-FPN.

Attention-guided Context Feature Pyramid Network for Object Detection

The paper "Attention-guided Context Feature Pyramid Network for Object Detection" introduces the Attention-guided Context Feature Pyramid Network (AC-FPN), a novel architecture designed to enhance object detection performance by resolving the dichotomy between feature map resolution and receptive field size in high-resolution inputs. Fundamentally, AC-FPN integrates attention-guided multi-path features to leverage discriminative information from various large receptive fields. This model's architecture comprises two primary modules: the Context Extraction Module (CEM) and the Attention-guided Module (AM), each executing critical roles in contextual information handling and attention mechanism respectively.

Context Extraction Module (CEM)

The Context Extraction Module (CEM) focuses on capturing extensive contextual information from diverse large receptive fields. Using multi-path dilated convolutional layers with varying dilation rates, CEM achieves effective scale variation in receptive fields. This setup facilitates comprehensive contextual insights without a significant hike in computational demands. A distinguishing feature of CEM is the use of deformable convolutional layers, which adaptively adjust to transformations in input data, thus offering robust feature extraction capabilities. Furthermore, CEM employs dense connections, promoting effective feature map integration across layers, allowing enhanced feature propagation and variation across scales.

Attention-guided Module (AM)

The Attention-guided Module (AM) incorporates a self-attention mechanism to accentuate salient dependencies and provide a more discriminative feature representation. This module encompasses two sub-modules: the Context Attention Module (CxAM) and the Content Attention Module (CnAM). CxAM is adept at modeling semantic relationships across spatial locations, thereby leveraging a robust context-based feature synthesis. Concurrently, CnAM mitigates issues pertaining to spatial information distortion, which arises due to receptive field manipulation in CEM, by restoring spatial details through locally attentive mechanisms. The fusion of CxAM and CnAM optimally balances contextual and spatial attentiveness, refining the overall feature set for precise object detection.

Results and Implications

The AC-FPN is seamlessly integrable with existing Feature Pyramid Network (FPN)-based models, extending their capabilities significantly as evidenced by the extensive empirical evaluations. Experimental results on the COCO dataset affirm that integrating AC-FPN with existing models notably enhances detection and segmentation tasks, marking a leap in Average Precision scores across multiple metrics. Such quantitative improvements reveal the model's adeptness at identifying larger objects, which traditionally pose challenges due to scale and occlusion in high-resolution settings.

While primarily applied to object detection, the broader implications of AC-FPN resonate across tasks requiring multiscale attention and precise feature localization, such as instance segmentation. By adapting AC-FPN into segmentation frameworks like Mask R-CNN, substantive gains are observed, suggesting its utility in a broader spectrum of computer vision applications.

Conclusion and Future Directions

AC-FPN signifies progress in addressing the challenging interplay between feature resolution and receptive field size. By establishing a framework that effectively utilizes attention mechanisms to deliver enhanced contextual understanding, it opens avenues for further research into optimizing attention-guided convolutional structures. Future work could explore augmenting AC-FPN's foundational concepts into other domains or advancing the efficiency of attention mechanisms, particularly in terms of computation and memory trade-offs. Additionally, extending such strategies to real-time applications can yield considerable benefits in edge-device deployments, enhancing their operational efficacy in complex visual environments.