4 citations · 8 across the 2 of their papers we have counts for
9 papers · 1 filter
Decomposed Prototype Learning for Few-Shot Scene Graph Generation
Xingchen Li, Jun Xiao, Guikun Chen +4
Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world appli…
Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
Xiaoshuang Huang, Hongxiang Li, Meng Cao +3
Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics. However, language-guided me…
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
Chaoyang Zhu, Long Chen
As the most fundamental scene understanding tasks, object detection and segmentation have made tremendous progress in deep learning era. Due to the expensive manual labeling cost,…
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation
Lin Li, Jun Xiao, Hanrong Shi +4
Nearly all existing scene graph generation (SGG) models have overlooked the ground-truth annotation qualities of mainstream SGG datasets, i.e., they assume: 1) all the manually ann…
Distributionally Generative Augmentation for Fair Facial Attribute Classification
Fengda Zhang, Qianpei He, Kun Kuang +5
Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting acc…
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Haiwen Diao, Bo Wan, Ying Zhang +3
Parameter-efficient transfer learning (PETL), i.e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains. To fu…