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Found 9 papers, 3 papers with code
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding.
Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation
We first propose source student backbone and target student backbone to respectively extract the source-style and target-style feature for both source and target images.
Look in Different Views: Multi-Scheme Regression Guided Cell Instance Segmentation
On the DSB2018 and CA2. 5, our network surpasses previous methods by 1. 2% (AP50).
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
To solve this problem, we propose a Multi-Modality Ovarian Tumor Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170 contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise annotations.
Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection
In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector.
Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification
On this issue, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main-branch in it.
MGML: Multi-Granularity Multi-Level Feature Ensemble Network for Remote Sensing Scene Classification
To avoid the interference from confusing information, we propose Multi-granularity Multi-Level Feature Ensemble Module (MGML-FEM) which can provide diverse predictions by full-channel feature generator (FC-FG).
Point Context: An Effective Shape Descriptor for RST-invariant Trajectory Recognition
Secondly, the shape context is nonlinearly mapped to a subspace by kernel nonparametric discriminant analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space.
Machine Translation Model based on Non-parallel Corpus and Semi-supervised Transductive Learning
Although the parallel corpus has an irreplaceable role in machine translation, its scale and coverage is still beyond the actual needs.
