In this paper, we introduce a novel network, called discriminative feature
network (DFNet), to address the unsupervised video object segmentation task. To
capture the inherent correlation among video frames, we learn discriminative
features (D-features) from the input images that reveal feature distribution
from a global perspective. The D-features are then used to establish
correspondence with all features of test image under conditional random field
(CRF) formulation, which is leveraged to enforce consistency between pixels.
The experiments verify that DFNet outperforms state-of-the-art methods by a
large margin with a mean IoU score of 83.4% and ranks first on the DAVIS-2016
leaderboard while using much fewer parameters and achieving much more efficient
performance in the inference phase. We further evaluate DFNet on the FBMS
dataset and the video saliency dataset ViSal, reaching a new state-of-the-art.
To further demonstrate the generalizability of our framework, DFNet is also
applied to the image object co-segmentation task. We perform experiments on a
challenging dataset PASCAL-VOC and observe the superiority of DFNet. The
thorough experiments verify that DFNet is able to capture and mine the
underlying relations of images and discover the common foreground objects.

本文提出了一种名为 DFNet 的新型网络，用于解决无监督视频物体分割任务，通过提取刻画全局特征分布的判别性特征，使用条件随机场 (CRF) 建立与测试图像的特征对应关系，并在像素级别上进行一致性约束。实验结果表明，该方法在使用更少的参数和更高效的推断性能的情况下，获得了优于当前最先进方法的 83.4% 的平均 IoU 分数，并在 DAVIS-2016 排行榜上排名第一。DFNet 进一步应用于图像对象协同分割任务，并在具有挑战性的数据集 PASCAL-VOC 上展示出其广泛适用性。