Global Context-Aware Progressive Aggregation Network for Salient Object Detection

Abstract

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple-level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Is Depth Really Necessary for Salient Object Detection?

“…Toward this end, we propose a novel Channel-Aware Fusion module (CAF), which adaptively selects the discriminative features for object understanding. Instead of using different specific structures for different aggregation strategies in previous works [7,37,42], we advocate using a generalized module to fuse any common types of features, e.g., features from different levels and features from different sources.…”

“…where denotes the weight of different level loss and is set as 5 with five stages in ResNet. Here we follow GCPANet [7] and set as [1, 0.8, 0.6, 0.4, 0.2].…”

“…We use SGD optimizer with the batch size=32 for 32 epochs. Inspired by [7,25], we adopt warm-up and linear decay strategies to adjust the learning rate with the maximum learning rate 0.005 for ResNet-50 backbone and 0.05 for other parts. We set momentum and decay rate to 0.9 and 5e-4, respectively.…”

“…As shown in Tab. 2, we compare our proposed DASNet with 10 state-of-the-art methods, i.e., BMPM [53], PAGR [54], R3Net [11], PiCANet [32], PoolNet [31], BANet [42], CPDNet [48], BASNet [38], F3Net [25], GCPANet [7]. As shown in Tab.…”

Pyramid Spatial Context Features for Salient Object Detection

“…Very recently, Liu and Han [35], [36] incorporated the global context feature to detect salient objects by adopting the spatial long short-term memory model. Zhao [15] adopts fully connected operations and Chen et al [46] uses global average pooling to obtain global information. The fully connected operations and spatial long short-term memory model are time-consuming and global average pooling can only obtain a single feature for all the pixel on the spatial domain of feature maps.…”