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Found 7 papers, 5 papers with code
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification
Efficient extraction of spectral sequences and geospatial information has always been a hot topic in hyperspectral image classification.
Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification
In this paper, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering
This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning.
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Ranked #69 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation
Specifically, we define the centripetal direction feature as a class of adjacent directions pointing to the nuclear center to represent the spatial relationship between pixels within the nucleus.
Effective Fusion Factor in FPN for Tiny Object Detection
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
Selective Sparse Sampling for Fine-Grained Image Recognition
Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e. g., beaks for bird species).
Ranked #32 on
Fine-Grained Image Classification
on Stanford Cars
Fine-Grained Image Classification
Fine-Grained Image Recognition
