Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognize the categories and counts of the items chosen by the customers. However, the training of such a system is challenged by the domain adaptation problem, in which the training data are images from isolated items while the testing images are for collections of items. Existing methods solve this problem with data augmentation using synthesized images, but the image synthesis leads to unreal images that affect the training process. In this paper, we propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to train the final visual item tallying network. Experiments on the large scale Retail Product Checkout (RPC) dataset demonstrate the superiority of the proposed method, i.e., we achieve 80.51 checkout accuracy compared with 56.68 of the baseline methods.
Salient object detection @cite_25 @cite_9 @cite_10 @cite_2 @cite_23 is to segment the main object in the image for pre-processing. Li al @cite_25 obtain the saliency map based on the multi-scale features extracted from CNN models. Hu al @cite_10 propose a saliency detection method based on the compactness hypothesis that assumes salient regions are more compact than background from the perspectives of both color layout and texture layout. Liu al @cite_9 develop a two-stage deep network, where a coarse prediction map is produced and followed by a recurrent CNN to refine the details of the prediction map hierarchically and progressively. Tang and Wu @cite_2 develop multiple single-scale fully convolutional networks integrated chained connections to generate saliency prediction results from coarse to fine. Recently, Hou al @cite_23 take full advantage of multi-level and multi-scale features extracted from fully CNNs, and introduce short connections to the skip-layer structures within the holistically-nested edge detector.
@cite_9 @cite_23 @cite_2 @cite_10 @cite_25
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Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this CVPR 2016 paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art. Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art. Deep convolutional neural networks (CNNs) have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patchwise (regionwise) training and inference or fully convolutional networks. Methods in the former category are generally time-consuming due to severe storage and computational redundancies among overlapping patches. To overcome this deficiency, methods in the second category attempt to directly map a raw input image to a predicted dense saliency map in a single network forward pass. Though being very efficient, it is arduous for these methods to detect salient objects of different scales or salient regions with weak semantic information. In this paper, we develop hybrid contrast-oriented deep neural networks to overcome the aforementioned limitations. Each of our deep networks is composed of two complementary components, including a fully convolutional stream for dense prediction and a segment-level spatial pooling stream for sparse saliency inference. We further propose an attentional module that learns weight maps for fusing the two saliency predictions from these two streams. A tailored alternate scheme is designed to train these deep networks by fine-tuning pretrained baseline models. Finally, a customized fully connected conditional random field model incorporating a salient contour feature embedding can be optionally applied as a postprocessing step to improve spatial coherence and contour positioning in the fused result from these two streams. Extensive experiments on six benchmark data sets demonstrate that our proposed model can significantly outperform the state of the art in terms of all popular evaluation metrics. In this paper, we proposed a novel method for effective salient object detection by designing a chained multi-scale fully convolutional network (CMSFCN). CMSFCN contained multiple single-scale fully convolutional networks (SSFCNs), which were integrated successively by using chained connections and generated saliency prediction results from coarse to fine. The chained connections not only combined the saliency prediction result from previous SSFCN with the input image of current SSFCN, but also combined the intermediate features from previous SSFCN and current SSFCN. With these chained connections, the sequential SSFCNs in CMSFCN automatically learned complemental and discriminative features to improve the saliency predictions progressively. Therefore, after jointly training CMSFCN with an end-to-end manner, precise saliency prediction results were produced under a coarse-to-fine behaviour. Compared with seven state-of-the-art CNN based salient object detection approaches over five benchmark datasets, experimental results demonstrated the efficiency and effectiveness of CMSFCN.