30 citations · 49 across the 8 of their papers we have counts for
8 papers
OV-VG: A Benchmark for Open-Vocabulary Visual Grounding
Chunlei Wang, Wenquan Feng, Xiangtai Li +5
Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is…
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision
Menghao Li, Chunlei Wang, Wenquan Feng +5
Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision tran…
Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation
Qi Zhao, Shuchang Lyu, Binghao Liu +2
Deep convolutional neural networks (DCNNs) based remote sensing (RS) image semantic segmentation technology has achieved great success used in many real-world applications such as…
MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation
Qi Zhao, Shuchang Lyu, Wenpei Bai +7
Ovarian cancer is one of the most harmful gynecological diseases. Detecting ovarian tumors in early stage with computer-aided techniques can efficiently decrease the mortality rate…
A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval
Qi Zhao, Xu Wang, Shuchang Lyu +2
Large-scale fine-grained image retrieval has two main problems. First, low dimensional feature embedding can fasten the retrieval process but bring accuracy reduce due to overlooki…
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation
Qi Zhao, Binghao Liu, Shuchang Lyu +1
Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges.…
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Binghao Liu, Yao Ding, Jianbin Jiao +2
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…
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
Boyu Yang, Mingbao Lin, Binghao Liu +4
Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem…