  Video copy localization aims to precisely localize all the copied segments
within a pair of untrimmed videos in video retrieval applications. Previous
methods typically start from frame-to-frame similarity matrix generated by
cosine similarity between frame-level features of the input video pair, and
then detect and refine the boundaries of copied segments on similarity matrix
under temporal constraints. In this paper, we propose TransVCL: an
attention-enhanced video copy localization network, which is optimized directly
from initial frame-level features and trained end-to-end with three main
components: a customized Transformer for feature enhancement, a correlation and
softmax layer for similarity matrix generation, and a temporal alignment module
for copied segments localization. In contrast to previous methods demanding the
handcrafted similarity matrix, TransVCL incorporates long-range temporal
information between feature sequence pair using self- and cross- attention
layers. With the joint design and optimization of three components, the
similarity matrix can be learned to present more discriminative copied
patterns, leading to significant improvements over previous methods on
segment-level labeled datasets (VCSL and VCDB). Besides the state-of-the-art
performance in fully supervised setting, the attention architecture facilitates
TransVCL to further exploit unlabeled or simply video-level labeled data.
Additional experiments of supplementing video-level labeled datasets including
SVD and FIVR reveal the high flexibility of TransVCL from full supervision to
semi-supervision (with or without video-level annotation). Code is publicly
available at https://github.com/transvcl/TransVCL.
