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نتیجه جستجو - فیوژن ویژگی

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Multi-model ensemble with rich spatial information for object detection
اثر گروهی چند مدلی با اطلاعات مکانی غنی برای ردیابی شی-2020
Due to the development of deep learning networks and big data dimensionality, research on ensemble deep learning is receiving an increasing amount of attention. This paper takes the object detection task as the research domain and proposes an object detection framework based on ensemble deep learning. To guarantee the accuracy as well as real-time detection, the detector uses a Single Shot MultiBox Detector (SSD) as the backbone and combines ensemble learning with context modeling and multi-scale feature representation. Two modes were designed in order to achieve ensemble learning: NMS Ensembling and Feature Ensembling. In addition, to obtain contextual information, we used dilated convolution to ex- pand the receptive field of the network. Compared with state-of-the-art detectors, our detector achieves superior performance on the PASCAL VOC set and the MS COCO set.
Keywords: Ensemble learning | Object detection | Dilated convolution | Feature fusion
مقاله انگلیسی
2 Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition-2019
Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs have been made for traditional face recognition by applying deep learning on a huge amount of labeled VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due to large domain difference as well as insufficient pairwise images in different modalities during training. In general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss of useful face information, resulting in a decrease in the performance of HFR problem. It is important to eliminate modal-related information and retain more facial identity information. In this paper, we propose a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both interand intra-class information for effectively training the deep model. To make full use of the various layers of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network for joint supervision training, which enables multiple layers of the network to obtain discriminative identity features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Keywords: Heterogeneous face recognition | Deep learning | Joint supervision loss | Feature fusion
مقاله انگلیسی
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بازدید امروز: 10601 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 10601 :::::::: افراد آنلاین: 58