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Found 13 papers, 2 papers with code
SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute Recognition
The AFSS module learns to provide reasonable scale prior information for different attribute groups, allowing the model to focus on different levels of feature maps with varying semantic granularity.
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection
Effective feature fusion of multispectral images plays a crucial role in multi-spectral object detection.
Ranked #2 on
Object Detection
on VEDAI
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath
Empirical results validate the performance of SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction task and node classification task.
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
The extensive research leveraging RGB-D information has been exploited in salient object detection.
Online Deep Learning based on Auto-Encoder
And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure; (3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes.
Multi-View representation learning in Multi-Task Scene
By this way, the original multi-task multi-view data has degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm.
Auto-Encoder based Co-Training Multi-View Representation Learning
As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views.
Adaptive Online Incremental Learning for Evolving Data Streams
The last one we often ignore is the learning of the latent representation.
Universal Transformer Hawkes Process with Adaptive Recursive Iteration
Enlighten by transformer model, which can learning sequence data efficiently without recurrent and convolutional structure, transformer Hawkes process is come out, and achieves state-of-the-art performance.
Temporal Attention Augmented Transformer Hawkes Process
With this in mind, we come up with a new kind of Transformer-based Hawkes process model, Temporal Attention Augmented Transformer Hawkes Process (TAA-THP), we modify the traditional dot-product attention structure, and introduce the temporal encoding into attention structure.
Attentive Multi-View Deep Subspace Clustering Net
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism.
Output fusion of MPC and PID and its application in intelligent layered water injection of oilfield
Secondly, combining the dynamic response characteristics and steady-state model of the whole-well water distribution equipment, a dynamic model of layered intelligent water injection is established, and the generation process of the wave code is defined; Finally, the MPC-PID optimal fusion control algorithm structure is designed to derive the fusion control law that minimizes the cost function under fixed weights, , and the optimal weights are calculated by combining the internal model structure of controller, so the optimization performance of each algorithm in the optimal fusion control is balanced.
A Multilingual Natural Stress Emotion Database
In this paper, we describe an ongoing effort in collecting and annotating a multilingual speech database of natural stress emotion from university students.
