1 citations · 1 across the 6 of their papers we have counts for
8 papers
Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
Runlong Cao, Ying Zang, Chuanwei Zhou +4
Semi-supervised referring expression segmentation (SS-RES) aims to achieve precise pixel-level language grounding under limited annotation, yet suffers from limited supervision and…
SPWOOD: Sparse Partial Weakly-Supervised Oriented Object Detection
Wei Zhang, Xiang Liu, Ningjing Liu +4
A consistent trend throughout the research of oriented object detection has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is partic…
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
Tianrun Chen, Runlong Cao, Xinda Yu +8
The rapid rise of large-scale foundation models has reshaped the landscape of image segmentation, with models such as Segment Anything achieving unprecedented versatility across di…
Semantic Discrepancy-aware Detector for Image Forgery Identification
Ziye Wang, Minghang Yu, Chunyan Xu +1
With the rapid advancement of image generation techniques, robust forgery detection has become increasingly imperative to ensure the trustworthiness of digital media. Recent resear…
LLM-Assisted Semantic Guidance for Sparsely Annotated Remote Sensing Object Detection
Wei Liao, Chunyan Xu, Chenxu Wang +1
Sparse annotation in remote sensing object detection poses significant challenges due to dense object distributions and category imbalances. Although existing Dense Pseudo-Label me…
MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation
Jialin Luo, Yuanzhi Wang, Ziqi Gu +7
Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stab…
Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection
Chenxu Wang, Chunyan Xu, Ziqi Gu +1
While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. We experimen…
Big-model Driven Few-shot Continual Learning
Ziqi Gu, Chunyan Xu, Zihan Lu +3
Few-shot continual learning (FSCL) has attracted intensive attention and achieved some advances in recent years, but now it is difficult to again make a big stride in accuracy due…