Long-tailed multi-label visual recognition (LTML) task is a highly
challenging task due to the label co-occurrence and imbalanced data
distribution. In this work, we propose a unified framework for LTML, namely
prompt tuning with class-specific embedding loss (LMPT), capturing the semantic
feature interactions between categories by combining text and image modality
data and improving the performance synchronously on both head and tail classes.
Specifically, LMPT introduces the embedding loss function with class-aware soft
margin and re-weighting to learn class-specific contexts with the benefit of
textual descriptions (captions), which could help establish semantic
relationships between classes, especially between the head and tail classes.
Furthermore, taking into account the class imbalance, the distribution-balanced
loss is adopted as the classification loss function to further improve the
performance on the tail classes without compromising head classes. Extensive
experiments are conducted on VOC-LT and COCO-LT datasets, which demonstrates
that the proposed method significantly surpasses the previous state-of-the-art
methods and zero-shot CLIP in LTML. Our codes are fully available at
https://github.com/richard-peng-xia/LMPT.

本研究提出了一种统一框架 —— 基于类特定嵌入损失的提示调整（LMPT）—— 用于解决多标记视觉识别中长尾类别的问题，结合文本和图像，引入嵌入损失函数，以类感知的软间距和重新权重为基础，学习特定于类别的上下文，采用分布平衡型损失函数作为分类损失函数，进一步提高了小类别的性能，与前人研究方法和零样本 CLIP 相比，结果表明所提出的方法显著优于先前的最先进方法。

LMPT：长尾多标签视觉识别中的类别特定嵌入损失的提示调整

LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed  Multi-Label Visual Recognition

Chest radiograph (CXR) interpretation in pediatric patients is error-prone
and requires a high level of understanding of radiologic expertise. Recently,
deep convolutional neural networks (D-CNNs) have shown remarkable performance
in interpreting CXR in adults. However, there is a lack of evidence indicating
that D-CNNs can recognize accurately multiple lung pathologies from pediatric
CXR scans. In particular, the development of diagnostic models for the
detection of pediatric chest diseases faces significant challenges such as (i)
lack of physician-annotated datasets and (ii) class imbalance problems. In this
paper, we retrospectively collect a large dataset of 5,017 pediatric CXR scans,
for which each is manually labeled by an experienced radiologist for the
presence of 10 common pathologies. A D-CNN model is then trained on 3,550
annotated scans to classify multiple pediatric lung pathologies automatically.
To address the high-class imbalance issue, we propose to modify and apply
"Distribution-Balanced loss" for training D-CNNs which reshapes the standard
Binary-Cross Entropy loss (BCE) to efficiently learn harder samples by
down-weighting the loss assigned to the majority classes. On an independent
test set of 777 studies, the proposed approach yields an area under the
receiver operating characteristic (AUC) of 0.709 (95% CI, 0.690-0.729). The
sensitivity, specificity, and F1-score at the cutoff value are 0.722
(0.694-0.750), 0.579 (0.563-0.595), and 0.389 (0.373-0.405), respectively.
These results significantly outperform previous state-of-the-art methods on
most of the target diseases. Moreover, our ablation studies validate the
effectiveness of the proposed loss function compared to other standard losses,
e.g., BCE and Focal Loss, for this learning task. Overall, we demonstrate the
potential of D-CNNs in interpreting pediatric CXRs.

本文研究了利用改进的 “分布平衡损失函数” 训练基于深度卷积神经网络（D-CNNs）的模型来自动分类儿科肺病的多个病理学表现，为解决医师标注样本稀缺、类别不平衡等问题，作者构建了一个包含 5,017 份儿科胸部 X 光片的数据集，并证明了这种方案在准确性上的优越性。

使用深度卷积神经网络在儿科胸部 X 光片中自动诊断多种疾病

Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest  Radiographs Using Deep Convolutional Neural Networks

We present a new loss function called Distribution-Balanced Loss for the
multi-label recognition problems that exhibit long-tailed class distributions.
Compared to conventional single-label classification problem, multi-label
recognition problems are often more challenging due to two significant issues,
namely the co-occurrence of labels and the dominance of negative labels (when
treated as multiple binary classification problems). The Distribution-Balanced
Loss tackles these issues through two key modifications to the standard binary
cross-entropy loss: 1) a new way to re-balance the weights that takes into
account the impact caused by label co-occurrence, and 2) a negative tolerant
regularization to mitigate the over-suppression of negative labels. Experiments
on both Pascal VOC and COCO show that the models trained with this new loss
function achieve significant performance gains over existing methods. Code and
models are available at: this https URL .

本文提出了一种称为分布平衡损失函数的新方法，用于解决多标签识别问题中遇到的长尾类分布、标签共现和负标签占主导的问题，并通过实验验证了该方法的有效性。