Video Semantic Object Segmentation by Self-Adaptation of DCNN
نویسندگان
چکیده
This paper proposes a new framework for semantic segmentation of objects in videos. We address the label inconsistency problem of deep convolutional neural networks (DCNNs) by exploiting the fact that videos have multiple frames; in a few frames the object is confidently-estimated (CE) and we use the information in them to improve labels of the other frames. Given the semantic segmentation results of each frame obtained from DCNN, we sample several CE frames to adapt the DCNN model to the input video by focusing on specific instances in the video rather than general objects in various circumstances. We propose offline and online approaches under different supervision levels. In experiments our method achieved great improvement over the original model and previous state-of-the-art methods. c © 2016 Elsevier Ltd. All rights reserved.
منابع مشابه
Learning Dense Convolutional Embeddings for Semantic Segmentation
This paper proposes a new deep convolutional neural network (DCNN) architecture for learning semantic segmentation. The main idea is to train the DCNN to produce internal representations that respect object boundaries. That is, for any two pixels on the same object, the DCNN is trained to produce nearly-identical internal representations; conversely, the DCNN is trained to produce dissimilar re...
متن کاملکاهش رنگ تصاویر با شبکههای عصبی خودسامانده چندمرحلهای و ویژگیهای افزونه
Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohene...
متن کاملSemi-supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation
Abstract. Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labelled datasets. However, for video semantic object segmentation, a domain where labels are scarce, e↵ectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pre-trained CNN image recog...
متن کاملSemantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called ”semantic image segmentation”). We show that responses at the final layer of DC...
متن کاملPer-Pixel Feedback for improving Semantic Segmentation
Semantic segmentation is the task of assigning a label to each pixel in the image.In recent years, deep convolutional neural networks have been driving advances in multiple tasks related to cognition. Although, DCNNs have resulted in unprecedented visual recognition performances, they offer little transparency. To understand how DCNN based models work at the task of semantic segmentation, we tr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1711.08180 شماره
صفحات -
تاریخ انتشار 2017