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Found 8 papers, 4 papers with code
Consistency-aware Self-Training for Iterative-based Stereo Matching
Iterative-based methods have become mainstream in stereo matching due to their high performance.
All-in-One: Transferring Vision Foundation Models into Stereo Matching
Inspired by the ability of vision foundation models (VFMs) to extract general representations, in this work, we propose AIO-Stereo which can flexibly select and transfer knowledge from multiple heterogeneous VFMs to a single stereo matching model.
A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users
Chinese emotion datasets are extremely scarce, and datasets capturing Chinese user personality traits are even more limited.
Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework
Text emotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning.
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification
Finally, the adaptive global-local fusion is proposed to dynamically combine global Mamba features and local convolution features for a global-local spectral-spatial representation.
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields.
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
To address this issue, we propose a novel diffusion-based feature learning framework that explores Multi-Timestep Multi-Stage Diffusion features for HSI classification for the first time, called MTMSD.
Union-net: A deep neural network model adapted to small data sets
However, the deep neural network model has complex structure, huge model parameters, and training requires more advanced equipment, which brings certain difficulties to the application.
