SOTA! A-R@1 0.005 A-R@5 0.02 A-R@10 0.05 Re-R@1 0.01 Re-R@5 0.03 Re-R@10 0.045 -2021-10 PyTorch CPU 查看项目 DAN- ON AffectNet 2021 SOTA! Accuracy (7 emotion) 65.69 Accuracy (8 emotion) 62.09 Convolution2021-09 PyTorch ...
Image Retrieval on DeepFashion Leaderboard Dataset View by RECALL@20RCCapsNetRCCapsNetOther modelsModels with highest Recall@2026. Aug84.6 Filter: untagged Edit Leaderboard RankModelRecall@20PaperCodeResultYearTags 1 RCCapsNet 84.6 Fashion Image Retrieval with Capsule Networks 2019 ...
Training Vision Transformers for Image Retrieval 表现SOTA!性能优于ProxyNCA++、XBM等网络,结果表明,与基于卷积的方法相比,transformer具有一致且显著的改进! Transformer杀疯了!近期又有一波视觉Transformer的工作(大都来自大厂和Top高校)。注2:整理不易,欢迎点赞,支持分享! Training Vision Transformers for Image Retr...
Image Retrieval on MS COCO Leaderboard Dataset View recall@1recall@5Recall@10QPS by Daterecall@5Recall@10QPS Created with Highcharts 9.3.0RECALL@1VisualSpartaVisualSpartaBLIP-2 ViT-G (fine-tuned)BLIP-2 ViT-G (fine-tuned)Other modelsModels with highest recall@120202022202420304050607080...
Jose, A., Lopez, R.D., Heisterklaus, I., Wien, M.: Pyramid pooling of convolutional feature maps for image retrieval. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 480–484. IEEE (2018) Qi, K., Guan, Q., Yang, C., Peng, F., Shen, S., Wu, ...
computer-vision re-ranking pytorch toolbox apex baseline image-search image-retrieval re-identification person-reidentification reids sota random-erasing open-reid person-reid Updated Jul 30, 2024 Python hora-search / hora Star 2.6k Code Issues Pull requests 🚀 efficient approximate nearest neigh...
Trained on 36.7M (query image, instruction, target image) triplets with rich semantic relations mined from the web, MagicLens achieves comparable or better results on eight benchmarks of various image retrieval tasks than prior state-of-the-art (SOTA) methods. Remarkably, it outperforms ...
我们在SIGIR 2023的论文《Rethinking Benchmarks for Cross-modal Image-text Retrieval》中用大量的实验证明,现在的一些图文检索的SOTA模型在细粒度的跨模态语义对齐上的表现还有很大提升空间。而细粒度的语义理解在实际工业界的应用中是非常重要的,这个问题目前尚未得到很好的解决。
在这项工作中,作者指出了当这些基于边际的分类损失与 GeM 池化相结合时的一个关键问题——作者用新的池化模块修复了这个问题。 Reranking for image retrieval.图像检索结果的重新排序传统上是通过局部特征匹配和几何验证(GV)[30,32,4]来完成的,最常与 RANSAC [12] 结合使用。现代深层局部特征 [27, 8] 也已...
图像搜索(Image Retrieval):使用语义信息在图像patches中检索与描述相匹配的图像候选patch。 目标定位(Object Localization):在候选图像中定位目标对象的位置,通常使用边界框或者图像分割来表示目标的位置。 1. Introduction 指出现有的VLM预训练的问题:基于图像级视觉表征的预训练模型并不能适应许多细粒度视觉任务 视觉识别...