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Global video object segmentation with spatial constraint module
We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent,...
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SAR ship detection network based on global context and multi-scale feature enhancement
With the rapid development of synthetic aperture radar (SAR) technology, SAR image ship detection plays a crucial role in fields such as marine...
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MAGNN-GC: Multi-head Attentive Graph Neural Networks with Global Context for Session-Based Recommendation
Session-based recommendation aims to predict the final preference of anonymous users based on their current session and global context. However,...
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Session-based recommendation with fusion of hypergraph item global and context features
Session-based recommendation (SBR) is to predict the items that users are likely to click afterward by using their recent click history. Learning...
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Camouflaged object detection based on context-aware and boundary refinement
Camouflaged Object Detection (COD) has been increasingly studied and the detection performance has been greatly improved based on deep learning...
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Target-aware pooling combining global contexts for aerial tracking
The UAVs captured targets are relatively small when compared with the ordinary surveillance cameras. Thus, a strong discriminative ability is...
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Multi-scale inputs and context-aware aggregation network for stereo matching
Despite the significant progress made in deep learning-based stereo matching, the accuracy of these methods significantly decreases when faced with...
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SiamDAG: Siamese dynamic receptive field and global context modeling network for visual tracking
Trackers based on anchor-free strategy have achieved a great success in recent years. However, they have limitations. To be specific, receptive...
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Attention-based global context network for driving maneuvers prediction
Driving maneuvers prediction is one of the most challenging tasks in modern Advanced Driver Assistance System. Such predictions can improve driving...
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Frame-level global context modeling for detection and localization of abnormality
Abnormality detection helps human beings by reducing the amount of data to be processed manually. However, detection and localization of contextual...
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STAM: a spatio-temporal adaptive module for improving static convolutions in action recognition
Temporal adaptive convolution has demonstrated superior performance over static convolution techniques in video understanding. However, it needs to...
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Transformer-Based Context Condensation for Boosting Feature Pyramids in Object Detection
Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) which aims to mitigate the gap between...
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GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendation
Trip recommendation, which aims to recommend a sequence of point-of-interests (POIs) as a trip to visit, is of great importance to location-based...
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A multimodal fusion-based deep learning framework combined with local-global contextual TCNs for continuous emotion recognition from videos
Continuous emotion recognition plays a crucial role in develo** friendly and natural human-computer interaction applications. However, there exist...
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GCPAN: an adaptive global cross-scale prior attention network for image super-resolution
Super-resolution has achieved remarkable results in recent years, which is attributed to the rapid development of convolutional neural networks...
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Camera module Lens blemish detection based on neural network interpretability
Lens blemish detection is an important link in camera module production. Automatic blemish detection for camera module Lens is a challenging task,...
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CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation
Automatic segmentation of head and neck (H&N) primary tumors in FDG-PET/CT images is significant for the treatment of cancer. In this paper, the...
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DoubleU-NetPlus: a novel attention and context-guided dual U-Net with multi-scale residual feature fusion network for semantic segmentation of medical images
Accurate segmentation of the region of interest in medical images can provide an essential pathway for devising effective treatment plans for...
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Video object segmentation based on temporal frame context information fusion and feature enhancement
At present, a large number of video object segmentation algorithms only use a small amount of frame information to guide the segmentation of the...
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Hierarchical decoding with latent context for image captioning
Mining more rich visual features and analyzing the context information from image for decoding part has become a challenging problem in image...
