  This paper addresses the task of unsupervised video multi-object
segmentation. Current approaches follow a two-stage paradigm: 1) detect object
proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching
for temporal association using re-identification techniques. However, the
generic features, widely used in both stages, are not reliable for
characterizing unseen objects, leading to poor generalization. To address this,
we introduce a novel approach for more accurate and efficient spatio-temporal
segmentation. In particular, to address \textbf{instance discrimination}, we
propose to combine foreground region estimation and instance grouping together
in one network, and additionally introduce temporal guidance for segmenting
each frame, enabling more accurate object discovery. For \textbf{temporal
association}, we complement current video object segmentation architectures
with a discriminative appearance model, capable of capturing more fine-grained
target-specific information. Given object proposals from the instance
discrimination network, three essential strategies are adopted to achieve
accurate segmentation: 1) target-specific tracking using a memory-augmented
appearance model; 2) target-agnostic verification to trace possible tracklets
for the proposal; 3) adaptive memory updating using the verified segments. We
evaluate the proposed approach on DAVIS$_{17}$ and YouTube-VIS, and the results
demonstrate that it outperforms state-of-the-art methods both in segmentation
accuracy and inference speed.
