5,644 research outputs found
ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain
We present ARCHANGEL; a novel distributed ledger based system for assuring
the long-term integrity of digital video archives. First, we describe a novel
deep network architecture for computing compact temporal content hashes (TCHs)
from audio-visual streams with durations of minutes or hours. Our TCHs are
sensitive to accidental or malicious content modification (tampering) but
invariant to the codec used to encode the video. This is necessary due to the
curatorial requirement for archives to format shift video over time to ensure
future accessibility. Second, we describe how the TCHs (and the models used to
derive them) are secured via a proof-of-authority blockchain distributed across
multiple independent archives. We report on the efficacy of ARCHANGEL within
the context of a trial deployment in which the national government archives of
the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
In this paper, we introduce a global video representation to video-based
person re-identification (re-ID) that aggregates local 3D features across the
entire video extent. Most of the existing methods rely on 2D convolutional
networks (ConvNets) to extract frame-wise deep features which are pooled
temporally to generate the video-level representations. However, 2D ConvNets
lose temporal input information immediately after the convolution, and a
separate temporal pooling is limited in capturing human motion in shorter
sequences. To this end, we present a \textit{global} video representation (3D
PersonVLAD), complementary to 3D ConvNets as a novel layer to capture the
appearance and motion dynamics in full-length videos. However, encoding each
video frame in its entirety and computing an aggregate global representation
across all frames is tremendously challenging due to occlusions and
misalignments. To resolve this, our proposed network is further augmented with
3D part alignment module to learn local features through soft-attention module.
These attended features are statistically aggregated to yield
identity-discriminative representations. Our global 3D features are
demonstrated to achieve state-of-the-art results on three benchmark datasets:
MARS \cite{MARS}, iLIDS-VID \cite{VideoRanking}, and PRID 2011Comment: Accepted to appear at IEEE Transactions on Neural Networks and
Learning System
Smart PIN: performance and cost-oriented context-aware personal information network
The next generation of networks will involve interconnection of heterogeneous individual
networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore,
there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s
characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches
for data delivery across the heterogeneous network are also required.
In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed:
Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD).
MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and
QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
In recent years, the explosion of web videos makes text-video retrieval
increasingly essential and popular for video filtering, recommendation, and
search. Text-video retrieval aims to rank relevant text/video higher than
irrelevant ones. The core of this task is to precisely measure the cross-modal
similarity between texts and videos. Recently, contrastive learning methods
have shown promising results for text-video retrieval, most of which focus on
the construction of positive and negative pairs to learn text and video
representations. Nevertheless, they do not pay enough attention to hard
negative pairs and lack the ability to model different levels of semantic
similarity. To address these two issues, this paper improves contrastive
learning using two novel techniques. First, to exploit hard examples for robust
discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module
(DMAE) to mine hard negative pairs from textual and visual clues. By further
introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively
identify all these hard negatives and explicitly highlight their impacts in the
training loss. Second, our work argues that triplet samples can better model
fine-grained semantic similarity compared to pairwise samples. We thereby
present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to
construct partial order triplet samples by automatically generating
fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL
designs an adaptive token masking strategy with cross-modal interaction to
model subtle semantic differences. Extensive experiments demonstrate that the
proposed approach outperforms existing methods on four widely-used text-video
retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.Comment: Accepted by ACM MM 202
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