Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and...
其中类 c 的动量典型表征(momentum prototype) \mu_c 是由预测类别为c 的归一化(normalized)查询表征(query embedding) q 的移动平均数来定义的。 \gamma 是一个可调的(tunable)超参数。 Synergy between contrastive learning and label disambiguation 上述的PiCO两个关键组成部分看似互相分离,但其实它们是以协作的...
We propose a contrastive label-based attention method (CLA) to associate each label with the most relevant image regions. Specifically, our label-based attention, guided by the latent label embedding, captures discriminative image details. To distinguish region-wise correlations, we implement a ...
给定样本 X ,后面的query和key embedding都由数据增强器 Aug(X) 而来。 原型标签与伪标签:PICO会为每个类别 c 维持一个原型向量 \mu_c;同样样本经过Encoder之后也会生成一个伪标签Pseudo Target pt_i 。如果 pt_i 和某个原型向量 \mu_c 接近,则此样本很可能属于类别 c。 两个模块: 标签消岐模块:标签消...
今天给大家介绍ICLR 2022最佳论文PICO: CONTRASTIVE LABEL DISAMBIGUATION FOR PARTIAL LABEL LEARNING,这篇文章解决的是Partial Label Learning(PLL)问题,即训练数据中一个图像不是一个确定的label,而是一组可能的label集合,需要预测出每个样本的真实label。
learning are unable to establish a robust semantic linkage between type labels and input word representations. There have been efforts to address this issue using contrastive learning, but for biomedical event detection such an approach faces challenges such as defining negative samples. To address ...
多任务学习“Embedding Label Structures for Fine-Grained Feature Representation”,程序员大本营,技术文章内容聚合第一站。
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets. [Paper][Code] WeLSA: Learning To Predict 6D Pose From Weakly Labeled Data Using Shape Alignment. [Paper] Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing....
We test our proposed label-retrieval-augmented diffusion model using two pre-trained encoders: (1) SimCLR [21]: We trained two encoders using the ResNet50 [53] architecture on the CIFAR-10 and CIFAR-100 datasets through contrastive learning; (2) CLIP [24]: the model is pre-trained on ...
Embedding contrastive unsupervised features to cluster in-and out-of-distribution noise in corrupted image datasets. In European Conference on Computer Vision (ECCV), 2022. [2] Paul Albert, Diego Ortego, Eric Arazo, Noel O'Connor, and Kevin McGuinness. Addressing out-of...