5,831 research outputs found
Instance Embedding Transfer to Unsupervised Video Object Segmentation
We propose a method for unsupervised video object segmentation by
transferring the knowledge encapsulated in image-based instance embedding
networks. The instance embedding network produces an embedding vector for each
pixel that enables identifying all pixels belonging to the same object. Though
trained on static images, the instance embeddings are stable over consecutive
video frames, which allows us to link objects together over time. Thus, we
adapt the instance networks trained on static images to video object
segmentation and incorporate the embeddings with objectness and optical flow
features, without model retraining or online fine-tuning. The proposed method
outperforms state-of-the-art unsupervised segmentation methods in the DAVIS
dataset and the FBMS dataset.Comment: To appear in CVPR 201
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
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