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Exploration and Exploitation of Unlabeled Data for Open-Set Semi-supervised Learning
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both...
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Inherit or discard: learning better domain-specific child networks from the general domain for multi-domain NMT
Multi-domain NMT aims to develop a parameter-sharing model for translating general and specific domains, such as biology, legal, etc., which often...
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DTS: dynamic training slimming with feature sparsity for efficient convolutional neural network
Deep convolutional neural networks have achieved remarkable progress on computer vision tasks over last years. In this paper, we proposed a dynamic...
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Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images
Designing a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet...
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IAFPN: interlayer enhancement and multilayer fusion network for object detection
Feature pyramid network (FPN) improves object detection performance by means of top-down multilevel feature fusion. However, the current FPN-based...
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GOA-net: generic occlusion aware networks for visual tracking
Occlusion is a frequent phenomenon that hinders the task of visual object tracking. Since occlusion can be from any object and in any shape, data...
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An improved multi-scale and knowledge distillation method for efficient pedestrian detection in dense scenes
Pedestrian detection in densely populated scenes, particularly in the presence of occlusions, remains a challenging issue in computer vision....
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A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of...
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Clustering with Minimum Spanning Trees: How Good Can It Be?
Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are...
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Self-representation with adaptive loss minimization via doubly stochastic graph regularization for robust unsupervised feature selection
Unsupervised feature selection (UFS), which involves selecting representative features from unlabeled high-dimensional data, has attracted much...
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ChemScraper: leveraging PDF graphics instructions for molecular diagram parsing
Most molecular diagram parsers recover chemical structure from raster images (e.g., PNGs). However, many PDFs include commands giving explicit...
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Lightweight safety helmet detection algorithm using improved YOLOv5
In response to the challenges faced by existing safety helmet detection algorithms when applied to complex construction site scenarios, such as poor...
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Online camera auto-calibration appliable to road surveillance
Camera calibration is an essential prerequisite for road surveillance applications, which determines the accuracy of obtaining three-dimensional...
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A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy
The cuckoo search algorithm (CS), an algorithm inspired by the nest-parasitic breeding behavior of cuckoos, has proved its own effectiveness as a...
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Low resource Twi-English parallel corpus for machine translation in multiple domains (Twi-2-ENG)
Although Ghana does not have one unique language for its citizens, the Twi dialect stands a chance of fulfilling this purpose. Twi is among the...
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A novel abstractive summarization model based on topic-aware and contrastive learning
The majority of abstractive summarization models are designed based on the Sequence-to-Sequence(Seq2Seq) architecture. These models are able to...
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Selfredepth
Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources....
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Low-dimensional intrinsic dimension reveals a phase transition in gradient-based learning of deep neural networks
Deep neural networks complete a feature extraction task by propagating the inputs through multiple modules. However, how the representations evolve...
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Cluster Validation Based on Fisher’s Linear Discriminant Analysis
Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and...
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Tree-managed network ensembles for video prediction
This paper presents an innovative approach that leverages a tree structure to effectively manage a large ensemble of neural networks for tackling...
