目标识别是指一个特殊目标(或一种类型的目标)从其它目标(或其它类型的目标)中被区分出来的过程。它既包括两个非常相似目标的识别,也包括一种类型的目标同其他类型目标的识别。

VIP内容

大多数的对象识别方法主要侧重于学习有判别性的视觉模式,而忽略了整体的物体结构。尽管很重要,但结构建模通常需要大量的手工注释,因此是劳动密集型的。在这篇论文中,我们提出通过将自我监督纳入传统的框架中来“观察对象”(明确而内在地对对象结构建模)。我们证明了在不增加额外注释和推理速度的情况下,识别主干可以被显著增强,从而实现更健壮的表示学习。具体来说,我们首先提出了一个对象范围学习模块,用于根据同一类别中实例间共享的视觉模式对对象进行本地化。然后,我们设计了一个空间上下文学习模块,通过预测范围内的相对位置,对对象的内部结构进行建模。这两个模块可以很容易地插入到任何骨干网络训练和分离的推理时间。大量的实验表明,我们的内视对象方法(LIO)在许多基准上获得了巨大的性能提升,包括通用对象识别(ImageNet)和细粒度对象识别任务(CUB、Cars、Aircraft)。我们还表明,这种学习范式可以高度泛化到其他任务,如对象检测和分割(MS COCO)。

成为VIP会员查看完整内容
0
28

最新论文

Inspired by the conclusion that human choose the visual cortex regions which corresponding to the real size of the object to analyze the features of the object, when realizing the objects in the real world. This paper presents a framework -- SizeNet which based on both the real sizes and the features of objects, to solve objects recognition problems. SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18 DenseNet-121. The results show that SizeNet provides much higher accuracy rates for the objects recognition than the other algorithms. SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object. This is because SizeNet recognizes the object based not only the features, but also the real size. The real size of the object can help to exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features. SizeNet is of great significance to the study of interpretable computer vision. Our code and dataset will be made public.

0
1
下载
预览
Top