  Few-shot video object segmentation (FS-VOS) aims at segmenting video frames
using a few labelled examples of classes not seen during initial training. In
this paper, we present a simple but effective temporal transductive inference
(TTI) approach that leverages temporal consistency in the unlabelled video
frames during few-shot inference. Key to our approach is the use of both global
and local temporal constraints. The objective of the global constraint is to
learn consistent linear classifiers for novel classes across the image
sequence, whereas the local constraint enforces the proportion of
foreground/background regions in each frame to be coherent across a local
temporal window. These constraints act as spatiotemporal regularizers during
the transductive inference to increase temporal coherence and reduce
overfitting on the few-shot support set. Empirically, our model outperforms
state-of-the-art meta-learning approaches in terms of mean intersection over
union on YouTube-VIS by 2.8%. In addition, we introduce improved benchmarks
that are exhaustively labelled (i.e. all object occurrences are labelled,
unlike the currently available), and present a more realistic evaluation
paradigm that targets data distribution shift between training and testing
sets. Our empirical results and in-depth analysis confirm the added benefits of
the proposed spatiotemporal regularizers to improve temporal coherence and
overcome certain overfitting scenarios.
