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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 |