Supervised High-Level Feature Learning With Label Consistencies for Object Recognition

2020 ◽  
Vol 58 (7) ◽  
pp. 4501-4516 ◽  
Author(s):  
Yuebin Wang ◽  
Xun Zhou ◽  
Honglei Yang ◽  
Liqiang Zhang ◽  
Suhong Liu ◽  
...  
Keyword(s):  
Object Recognition ◽  
Feature Learning ◽  
High Level ◽  
High Level Feature

scholarly journals Domain Conditioned Adaptation Network

2020 ◽  
Vol 34 (07) ◽  
pp. 11386-11393 ◽  
Author(s):  
Shuang Li ◽  
Chi Liu ◽  
Qiuxia Lin ◽  
Binhui Xie ◽  
Zhengming Ding ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Feature Learning ◽  
Feature Representations ◽  
Domain Specific ◽  
Cross Domain ◽  
Learning Tasks ◽  
Domain Learning ◽  
High Level ◽  
High Level Feature ◽  
Target Data

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.

An Uncertainty-based Neural Network for Explainable Trajectory Segmentation

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Author(s):  
Xin Bi ◽  
Chao Zhang ◽  
Fangtong Wang ◽  
Zhixun Liu ◽  
Xiangguo Zhao ◽  
...  
Keyword(s):  
Real World ◽  
Feature Learning ◽  
Learning Performance ◽  
Bed Net ◽  
Time Series Segmentation ◽  
Real World Applications ◽  
Real World Datasets ◽  
High Level ◽  
High Level Feature ◽  
Trajectory Segmentation

As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse results. Although deep neural networks have tremendous advantages in terms of high-level feature learning performance, deploying as a blackbox seriously limits the real-world applications. Providing explainable segmentations has significance for result evaluation and decision making. Thus, in this article, we address trajectory segmentation by proposing a Bayesian Encoder-Decoder Network (BED-Net) to provide accurate detection with explainability and references for the following active-learning procedures. BED-Net consists of a segmentation module based on Monte Carlo dropout and an explanation module based on uncertainty learning that provides results evaluation and visualization. Experimental results on both benchmark and real-world datasets indicate that BED-Net outperforms the rival methods and offers excellent explainability in the applications of trajectory segmentation.

scholarly journals Person Re-Identification Based on Attention Mechanism and Context Information Fusion

2021 ◽  
Vol 13 (3) ◽  
pp. 72
Author(s):  
Shengbo Chen ◽  
Hongchang Zhang ◽  
Zhou Lei
Keyword(s):  
Information Fusion ◽  
Irrelevant Information ◽  
Features Extraction ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Context Information ◽  
Feature Map ◽  
High Level ◽  
Surveillance Analysis ◽  
High Level Feature

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

Deep AI Enabled Ubiquitous Wireless Sensing

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Chenning Li ◽  
Zhichao Cao ◽  
Yunhao Liu
Keyword(s):  
Great Success ◽  
Wireless Sensing ◽  
Ubiquitous Sensing ◽  
Sensing Systems ◽  
The Status ◽  
Sensing Model ◽  
High Level ◽  
Traditional Approaches ◽  
The Internet Of Things ◽  
High Level Feature

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

Entropy and orthogonality based deep discriminative feature learning for object recognition

2018 ◽  
Vol 81 ◽  
pp. 71-80 ◽  
Author(s):  
Weiwei Shi ◽  
Yihong Gong ◽  
De Cheng ◽  
Xiaoyu Tao ◽  
Nanning Zheng
Keyword(s):  
Object Recognition ◽  
Feature Learning ◽  
Discriminative Feature

scholarly journals A New Paradigm for Training Hyperactive Dopamine Transporter Knockout Rats: Influence of Novel Stimuli on Object Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Natalia P. Kurzina ◽  
Anna B. Volnova ◽  
Irina Y. Aristova ◽  
Raul R. Gainetdinov
Keyword(s):  
Object Recognition ◽  
Dopamine Transporter ◽  
Cognitive Task ◽  
Recognition Task ◽  
Long Term Memory ◽  
New Paradigm ◽  
Dopaminergic Transmission ◽  
Novel Objects ◽  
High Level ◽  
The Brain

Attention deficit hyperactivity disorder (ADHD) is believed to be connected with a high level of hyperactivity caused by alterations of the control of dopaminergic transmission in the brain. The strain of hyperdopaminergic dopamine transporter knockout (DAT-KO) rats represents an optimal model for investigating ADHD-related pathological mechanisms. The goal of this work was to study the influence of the overactivated dopamine system in the brain on a motor cognitive task fulfillment. The DAT-KO rats were trained to learn an object recognition task and store it in long-term memory. We found that DAT-KO rats can learn to move an object and retrieve food from the rewarded familiar objects and not to move the non-rewarded novel objects. However, we observed that the time of task performance and the distances traveled were significantly increased in DAT-KO rats in comparison with wild-type controls. Both groups of rats explored the novel objects longer than the familiar cubes. However, unlike controls, DAT-KO rats explored novel objects significantly longer and with fewer errors, since they preferred not to move the non-rewarded novel objects. After a 3 months’ interval that followed the training period, they were able to retain the learned skills in memory and to efficiently retrieve them. The data obtained indicate that DAT-KO rats have a deficiency in learning the cognitive task, but their hyperactivity does not prevent the ability to learn a non-spatial cognitive task under the presentation of novel stimuli. The longer exploration of novel objects during training may ensure persistent learning of the task paradigm. These findings may serve as a base for developing new ADHD learning paradigms.

scholarly journals Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

2020 ◽  
Author(s):  
Alexander J.E. Kell ◽  
Sophie L. Bokor ◽  
You-Nah Jeon ◽  
Tahereh Toosi ◽  
Elias B. Issa
Keyword(s):  
Object Recognition ◽  
Model Organism ◽  
High Standard ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Neuroscience ◽  
Brain Functions ◽  
Invariant Object Recognition ◽  
Small Model ◽  
High Level

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

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