21 citations · 72 across the 13 of their papers we have counts for
6 papers · 1 filter
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning
Xuwei Tan, Yi-Jie Huang, Yaqian Li
Semi-supervised Learning (SSL) has been proven vulnerable to out-of-distribution (OOD) samples in realistic large-scale unsupervised datasets due to over-confident pseudo-labeling…
CLIP Brings Better Features to Visual Aesthetics Learners
Liwu Xu, Jinjin Xu, Yuzhe Yang +3
Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations. Recent large-scale vision-language models, such as Contrastiv…
Recognize Anything: A Strong Image Tagging Model
Youcai Zhang, Xinyu Huang, Jinyu Ma +9
We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for large models in computer vision, demonstrating the zero…
Box-Level Active Detection
Mengyao Lyu, Jundong Zhou, Hui Chen +7
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchma…
Knowledge Distillation from Single to Multi Labels: an Empirical Study
Youcai Zhang, Yuzhuo Qin, Hengwei Liu +3
Knowledge distillation (KD) has been extensively studied in single-label image classification. However, its efficacy for multi-label classification remains relatively unexplored. I…
Tag2Text: Guiding Vision-Language Model via Image Tagging
Xinyu Huang, Youcai Zhang, Jinyu Ma +6
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic…