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Found 10 papers, 4 papers with code
TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers.
HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection
To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level.
Scribble-Supervised RGB-T Salient Object Detection
To further polish the expanded labels, we propose a prediction module to alleviate the sharpness of boundary.
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation
The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM). Recent supervised deep learning-based methods have achieved great success in a such classification task. However, those methods require enough labeled samples to work properly and in many circumstances, it is not available. To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels. Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features. Next, the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features. And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation.
Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform
As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications.
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Further, the block sparse recovery property is analyzed for wide bandwidth signals.
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper.
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression
In addition, most approaches ignore the spatial and channel redundancy.
A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling
The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate.
