论文标题
Snakeclef 2022中的细粒和长尾蛇识别的解决方案
Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022
论文作者
论文摘要
自动蛇种识别很重要，因为它具有巨大的潜力，可以帮助蛇虫引起的降低死亡和残疾。我们在Snakeclef 2022中介绍了解决方案，以在重度长尾分配中识别细粒蛇物种。首先，网络体系结构旨在从多种模式中提取和融合特征，即来自语言模式的视觉方式和地理局部信息的照片。然后，研究了基于logit调整的方法，以减轻严重的类失衡引起的影响。接下来，提出了有监督的学习方法的结合，以充分利用数据集，包括标记的培训数据和未标记的测试数据。最后，采用后处理策略，例如多尺度和多曲线测试时间启动，位置过滤和模型集合，以提高性能。凭借几种不同模型的合奏，在最终排行榜上获得了82.65％的私人分数，排名第三。
Automatic snake species recognition is important because it has vast potential to help lower deaths and disabilities caused by snakebites. We introduce our solution in SnakeCLEF 2022 for fine-grained snake species recognition on a heavy long-tailed class distribution. First, a network architecture is designed to extract and fuse features from multiple modalities, i.e. photograph from visual modality and geographic locality information from language modality. Then, logit adjustment based methods are studied to relieve the impact caused by the severe class imbalance. Next, a combination of supervised and self-supervised learning method is proposed to make full use of the dataset, including both labeled training data and unlabeled testing data. Finally, post processing strategies, such as multi-scale and multi-crop test-time-augmentation, location filtering and model ensemble, are employed for better performance. With an ensemble of several different models, a private score 82.65%, ranking the 3rd, is achieved on the final leaderboard.
