Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks. Instead of generating thousands of candidate bounding boxes and refining them, our network directly learns to generate the saliency map containing the exact number of salient objects. During training, we convert the ground-truth rectangular boxes to Gaussian distributions that better capture the ROI regarding individual salient objects. During inference, the network predicts Gaussian distributions centered at salient objects with an appropriate covariance, from which bounding boxes are easily inferred. Notably, our network performs saliency map prediction without pixel-level annotations, salient object detection without object proposals, and salient object subitizing simultaneously, all in a single pass within a unified framework. Extensive experiments show that our approach outperforms existing methods on various datasets by a large margin, and achieves more than 100 fps with VGG16 network on a single GPU during inference

LARNet:Towards Lightweight, Accurate and Real-time Salient Object Detection

Salient object detection (SOD) has rapidly developed in recent years, and detection performance has greatly improved. However, the price of these improvements is increasingly complex networks that require more computing resources and sacrifice real-time performance. This makes it difficult to deploy these approaches on devices with limited computing resources (such as mobile phones, embedded platforms, etc.). Considering recently developed lightweight SOD models, their detection and real-time performance are always compromised in demanding practical application scenarios. To solve these problems, we propose a novel lightweight SOD method called LARNet and its corresponding extremely lightweight method LARNet* according to application requirements. These methods balance the relationship between lightweight requirements, detection accuracy and real-time performance. First, we propose a saliency backbone network tailored for SOD, which removes the need for pre-training with ImageNet and effectively reduces feature redundancy. Subsequently, we propose a novel context gating module (CGM), which simulates the physiological mechanism of human brain neurons and visual information processing, and realizes the deep fusion of multilevel features at the global level. Finally, the saliency map is output after fusion of multi-level features. Extensive experiments on popular benchmark datasets demonstrate that the proposed LARNet (LARNet*) achieves 98 (113) FPS on a GPU and 3 (6) FPS on a CPU. With approximately 680K (90K) parameters, the model has significant performance advantages over (extremely) lightweight methods, even surpassing some heavyweight model

Salient region detection using contrast-based saliency and watershed segmentation

Salient region detection is useful for many applications such as image segmentation, compression, image retrieval, object tracking, and machine vision systems.In this paper, an approach to detect salient regions in a visual scene using contrast-based saliency and watershed segmentation is presented.The approach allows salient objects to be detected and extracted for analysis while preserving the actual boundaries of the salient objects. The approach can be executed in parallel making it efficient for real time applications

Real Time Image Saliency for Black Box Classifiers

In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on CIFAR-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods