对于目标级训练,我们使用总共约200万张图像的公开可用检测数据集(如图所示,OpenImages V4 (OI) [24]、Objects 365 (O365) [35]和/或Visual Genome (VG) [23])。对COCO [27]、LVIS [13]和O365进行评估。有关数据集的详细信息,请参阅附录A1.2。 4.3 Open-Vocabulary Detection Performance 我们使用LVIS v1...
论文地址:Simple Open-Vocabulary Object Detection with Vision Transformers 代码链接:github.com/google-resea 简单总结: 本文出发点是想利用现有的图文大模型(比如CLIP)的强大表征能力做目标检测任务,尤其是在长尾问题(long-tailed)和开放词汇(Open-Vocabulary)的setting。作者借助CLIP的基本架构,具体则是将图片级分类改...
OWL-ViT 是谷歌于 22 年 5 月提出的一种新的 OVD(Open Vocabulary Detection)算法。传统的检测算法会收到训练时标注类别的限制,无法在推理时检测出训练集中未出现的类别;而 OVD 算法,在推理时可以检测由开放词表定义的任意新类。 在图像分类任务中,通过将简单的模型结构与大规模预训练相结合(如 CLIP),即可在...
For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection....
Described Object Detection Image Classification Object Object Detection One-Shot Object Detection Open-vocabulary object detection Open Vocabulary Object Detection Datasets Edit MS COCO LVIS Objects365 Description Detection Dataset Results from the Paper Edit Ranked #1 on One-Shot Object Detection on MS...
4.2. Open-Vocabulary Benchmarking 4.3. Direct and Task-Specific Transfer 4.4. Segmentation and Detection in the Wild 4.5. Ablation 5. Conclusion 我们提出了OpenSeeD,这是一个简单的开放式词汇分割和检测框架,它使用单个模型从不同的分割和检测数据集中联合学习。为了弥补前台目标和后台对象之间的任务差距,我们...
Open-vocabulary object detection using captions. In CVPR, 2021. 1 [51] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vi- sion language pre-training: Aligning texts with visual con- cepts. In ICML, 2022. 2 [52] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Ch...
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We now have the vocabulary of cup, cap, R, and R―. Any knot or link can be written as a composition of these fragments, and consequently a choice of such mappings determines an amplitude for knots and links. In order for such an amplitude to be topological we want it to be invariant...
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation 6016 17:00 ChatGPT生成文本检测 5211 21:00 Invariant Learning via Probability of Sufficient and Necessary Causes 6019 25:00 Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss 6017 23:00 Pre-training...