这就引出了零样本目标检测(Zero-shot Object Detection)和开放词汇目标检测(Open-vocabulary Object Detection)这两个具有前沿意义的研究方向,即让模型拥有在没有见过特定类别的情况下识别新类型的目标的能力。 由于这两个概念经常存在交叉和混用,本文统一将能够实现零样本检测、目标定位以及通过视觉提示完成少样本推理
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...
4.3 Open-Vocabulary Detection Performance 我们使用LVIS v1.0 val [13]作为我们的主要基准,因为该数据集有一条罕见类别的长尾,因此非常适合测量开放词汇表的性能。为了进行评估,我们使用所有类别名称作为每个图像的查询,即LVIS中每个图像的1203个查询。如第4.6节所述,类预测通过七个提示词模板进行组合。一些LVIS类别出...
简介:Open-Vocabulary Object Detection (OVD)可以翻译为**“面向开放词汇下的目标检测”,**该任务和 zero-shot object detection 非常类似,核心思想都是在可见类(base class)的数据上进行训练,然后完成对不可见类(unseen/ target)数据的识别和检测,除了核心思想类似外,很多论文其实对二者也没有进行很好的区分。 一...
四、Conditional Matching for Open-Vocabulary Detection 为了使DETR超越闭集分类并执行开放词汇检测,我们为Transformer解码器配备了条件输入,并将学习目标重新表述为二进制匹配问题。 4.1 Conditional Inputs 给定一个具有所有训练(基)类的标准注释的对象检测数据集,我们需要将这些注释转换为条件输入,以促进我们的新训练范式...
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...
deep-learning pytorch semantic-segmentation zero-shot-learning instance-segmentation panoptic-segmentation open-world-classification diffusion-models text-image-retrieval open-vocabulary open-vocabulary-semantic-segmentation open-world-object-detection open-vocabulary-segmentation Updated Jul 6, 2024 Python Skals...
Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We ide...
Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393–14402, 2021. [77] Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang. Cross-modal contrastive learning for text-to-image ge...
Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned ...