Person Search in Videos with One Portrait Through Visual and Temporal Links

Huang, Qingqiu; Li, Wentao; Lin, Dahua

doi:10.1007/978-3-030-01261-8_26

Cited by 58 publications

(48 citation statements)

References 46 publications

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“…2. UGG vs PPCC [11]: Using the same input similarity without training, UGG-U performs better than PPCC with relatively large margin, especially in the ACROSS protocol. Since in the ACROSS protocol, queries are searched among tracklets from all movies, the connections based on body appearance are not reliable across movies as those in the IN protocol.…”

Section: Evaluation On the Proposed Ugg Methodsmentioning

confidence: 99%

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Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

Zheng

Rong

Chen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity from highquality faces to low-quality ones through contextual connections, which are constructed based on context such as body appearance. However, previous methods have often propagated erroneous information due to lack of uncertainty modeling of the noisy contextual connections. In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph. UGG explicitly models the uncertainty of the contextual connections by adaptively updating the weights of the edge gates according to the identity distributions of the nodes during inference. UGG is a generic graphical model that can be applied at only inference time or with end-to-end training. We demonstrate the effectiveness of UGG with state-of-theart results in the recently released challenging Cast Search in Movies and IARPA Janus Surveillance Video Benchmark dataset.

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Section: Evaluation On the Proposed Ugg Methodsmentioning

confidence: 99%

“…Another is ACROSS which search among tracklets in all the movies in the testing set. Please refer [11] for more details.…”

Section: Csmmentioning

confidence: 99%

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Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

Zheng

Rong

Chen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Some smaller datasets for recognition and retrieval in movies and TV shows have also been used in the literature. We can mention the Buffy the Vampire Slayer [66], The Big Bang Theory [67] and Sherlock [68] datasets, or the Cast Search in Movies (CSM) dataset [69]. However, they are either of small size or only contain cropped tracklets, posing the same problems of some of the datasets described earlier.…”

Section: Large-scale Video Datasets For Face Recognition and Retrievalmentioning

confidence: 99%

A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

Ciaparrone

Chiariglione

Tagliaferri

2022

Neural Comput & Applic

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Face-based video retrieval (FBVR) is the task of retrieving videos that containing the same face shown in the query image. In this article, we present the first end-to-end FBVR pipeline that is able to operate on large datasets of unconstrained, multi-shot, multi-person videos. We adapt an existing audiovisual recognition dataset to the task of FBVR and use it to evaluate our proposed pipeline. We compare a number of deep learning models for shot detection, face detection, and face feature extraction as part of our pipeline on a validation dataset made of more than 4000 videos. We obtain 97.25% mean average precision on an independent test set, composed of more than 1000 videos. The pipeline is able to extract features from videos at $$\sim $$ ∼ 7 times the real-time speed, and it is able to perform a query on thousands of videos in less than 0.5 s.

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“…Xia et al [244] propose an online multi-modal searching machine(OMS) to search persons in videos with their features of face, body and voice. Similar work on person searching in video includes [245] [246].…”

Section: Future Workmentioning

confidence: 99%

AI Video Editing: a Survey

Zhang¹,

Li²,

Han³

et al. 2022

Preprint

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Video editing is a high-required job, for it requires skilled artists or workers equipped with plentiful physical strength and multidisciplinary knowledge, such as cinematography, aesthetics. Thus gradually, more and more researches focus on proposing semi-automatical and even fully automatical solutions to reduce workloads. Since those conventional methods are usually designed to follow some simple guidelines, they lack flexibility and capability to learn complex ones. Fortunately, the advances of computer vision and machine learning make up the shortages of traditional approaches and make AI editing feasible. There is no survey to conclude those emerging researches yet. This paper summaries the development history of automatic video editing, and especially the applications of AI in partial and full workflows. We emphasizes video editing and discuss related works from multiple aspects: modality, type of input videos, methology, optimization, dataset, and evaluation metric. Besides, we also summarize the progresses in image editing domain, i.e., style transferring, retargeting, and colorization, and seek for the possibility to transfer those techniques to video domain. Finally, we give a brief conclusion about this survey and explore some open problems.

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Person Search in Videos with One Portrait Through Visual and Temporal Links

Cited by 58 publications

References 46 publications

Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

Uncertainty Modeling of Contextual-Connections Between Tracklets for Unconstrained Video-Based Face Recognition

A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

AI Video Editing: a Survey

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