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Found 9 papers, 3 papers with code
USTC-KXDIGIT System Description for ASVspoof5 Challenge
In Track 2, we continued using the CM system from Track 1 and fused it with a CNN-based ASV system.
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
To tackle the spectral and spatial redundancy challenges in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA.
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
Aerial Scene Classification
Building change detection for remote sensing images
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SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS.
THUEE system description for NIST 2020 SRE CTS challenge
This paper presents the system description of the THUEE team for the NIST 2020 Speaker Recognition Evaluation (SRE) conversational telephone speech (CTS) challenge.
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
In the pre-training stage we reserve the speaker weights, and there are no positive samples to train them in this stage.
The SpeakIn System Description for CNSRC2022
In the SV task fixed track, our system was a fusion of five models, and two models were fused in the SV task open track.
Poformer: A simple pooling transformer for speaker verification
Most recent speaker verification systems are based on extracting speaker embeddings using a deep neural network.
Fast query-by-example speech search using separable model
Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality.
