28 citations · 57 across the 13 of their papers we have counts for
13 papers
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Bin-Bin Gao, Xiaochen Chen, Zhongyi Huang +6
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The ex…
Pseudo-label Alignment for Semi-supervised Instance Segmentation
Jie Hu, Chen Chen, Liujuan Cao +4
Pseudo-labeling is significant for semi-supervised instance segmentation, which generates instance masks and classes from unannotated images for subsequent training. However, in ex…
Improving Human-Object Interaction Detection via Virtual Image Learning
Shuman Fang, Shuai Liu, Jie Li +3
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks. Howe…
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Qiong Wu, Shubin Huang, Yiyi Zhou +4
Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP…
InterFormer: Real-time Interactive Image Segmentation
You Huang, Hao Yang, Ke Sun +4
Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suff…
Towards Efficient Visual Adaption via Structural Re-parameterization
Gen Luo, Minglang Huang, Yiyi Zhou +4
Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at inexpensively adapting large-scale pre-trained models to downstream tasks. Recent advances have a…
X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance
Yiwei Ma, Xiaioqing Zhang, Xiaoshuai Sun +5
Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text. P…
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Wu +8
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CN…
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
Jiawei Zhan, Jun Liu, Wei Tang +8
Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Al…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
Class-Aware Contrastive Semi-Supervised Learning
Fan Yang, Kai Wu, Shuyi Zhang +6
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the nois…
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Zhiwei Chen, Changan Wang, Yabiao Wang +6
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result i…
OMPQ: Orthogonal Mixed Precision Quantization
Yuexiao Ma, Taisong Jin, Xiawu Zheng +6
To bridge the ever increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest…