103 research outputs found
Circulant temporal encoding for video retrieval and temporal alignment
We address the problem of specific video event retrieval. Given a query video
of a specific event, e.g., a concert of Madonna, the goal is to retrieve other
videos of the same event that temporally overlap with the query. Our approach
encodes the frame descriptors of a video to jointly represent their appearance
and temporal order. It exploits the properties of circulant matrices to
efficiently compare the videos in the frequency domain. This offers a
significant gain in complexity and accurately localizes the matching parts of
videos. The descriptors can be compressed in the frequency domain with a
product quantizer adapted to complex numbers. In this case, video retrieval is
performed without decompressing the descriptors. We also consider the temporal
alignment of a set of videos. We exploit the matching confidence and an
estimate of the temporal offset computed for all pairs of videos by our
retrieval approach. Our robust algorithm aligns the videos on a global timeline
by maximizing the set of temporally consistent matches. The global temporal
alignment enables synchronous playback of the videos of a given scene
LAMV: Learning to align and match videos with kernelized temporal layers
This paper considers a learnable approach for comparing and aligning videos. Our architecture builds upon and revisits temporal match kernels within neural networks: we propose a new temporal layer that finds temporal alignments by maximizing the scores between two sequences of vectors, according to a time-sensitive similarity metric parametrized in the Fourier domain. We learn this layer with a temporal proposal strategy, in which we minimize a triplet loss that takes into account both the localization accuracy and the recognition rate. We evaluate our approach on video alignment, copy detection and event retrieval. Our approach outperforms the state on the art on temporal video alignment and video copy detection datasets in comparable setups. It also attains the best reported results for particular event search, while precisely aligning videos
VADER: Video Alignment Differencing and Retrieval
We propose VADER, a spatio-temporal matching, alignment, and change
summarization method to help fight misinformation spread via manipulated
videos. VADER matches and coarsely aligns partial video fragments to candidate
videos using a robust visual descriptor and scalable search over adaptively
chunked video content. A transformer-based alignment module then refines the
temporal localization of the query fragment within the matched video. A
space-time comparator module identifies regions of manipulation between aligned
content, invariant to any changes due to any residual temporal misalignments or
artifacts arising from non-editorial changes of the content. Robustly matching
video to a trusted source enables conclusions to be drawn on video provenance,
enabling informed trust decisions on content encountered
Surgical video retrieval using deep neural networks
Although the amount of raw surgical videos, namely videos
captured during surgical interventions, is growing fast, automatic retrieval
and search remains a challenge. This is mainly due to the nature
of the content, i.e. visually non-consistent tissue, diversity of internal organs,
abrupt viewpoint changes and illumination variation. We propose
a framework for retrieving surgical videos and a protocol for evaluating
the results. The method is composed of temporal shot segmentation and
representation based on deep features, and the protocol introduces novel
criteria to the field. The experimental results prove the superiority of
the proposed method and highlight the path towards a more effective
protocol for evaluating surgical videos
- …