Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers

The problem of Near-Duplicate Video Retrieval (NDVR) has attracted increasing interest due to the huge growth of video content on the Web, which is characterized by high degree of near duplicity. This calls for efficient NDVR approaches. Motivated by the outstanding performance of Convolutional Neural Networks (CNNs) over a wide variety of computer vision problems, we leverage intermediate CNN features in a novel global video representation by means of a layer-based feature aggregation scheme. We perform extensive experiments on the widely used CC_WEB_VIDEO dataset, evaluating three popular deep architectures (AlexNet, VGGNet, GoogLeNet) and demonstrating that the proposed approach exhibits superior performance over the state-of-the-art, achieving a mean Average Precision (mAP) score of 0.976

FIVR: Fine-Grained Incident Video Retrieval

This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devise a protocol for the annotation of the dataset with respect to the four types of video associations defined by FIVR. Finally, we report the results of an experimental study on the dataset comparing five state-of-the-art methods developed based on a variety of visual descriptors, highlighting the challenges of the current problem

DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval

In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose either: (i) fine-grained approaches employing spatio-temporal representations and similarity calculations, achieving high performance at a high computational cost or (ii) coarse-grained approaches representing/indexing videos as global vectors, where the spatio-temporal structure is lost, providing low performance but also having low computational cost. In this work, we propose a Knowledge Distillation framework, which we call Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: a) Student Networks at different retrieval performance and computational efficiency trade-offs and b) a Selection Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency. We train several students with different architectures and arrive at different trade-offs of performance and efficiency, i.e., speed and storage requirements, including fine-grained students that store index videos using binary representations. Importantly, the proposed scheme allows Knowledge Distillation in large, unlabelled datasets -- this leads to good students. We evaluate DnS on five public datasets on three different video retrieval tasks and demonstrate a) that our students achieve state-of-the-art performance in several cases and b) that our DnS framework provides an excellent trade-off between retrieval performance, computational speed, and storage space. In specific configurations, our method achieves similar mAP with the teacher but is 20 times faster and requires 240 times less storage space. Our collected dataset and implementation are publicly available: https://github.com/mever-team/distill-and-select

Fine-grained Incident Video Retrieval with Video Similarity Learning.

PhD ThesesIn this thesis, we address the problem of Fine-grained Incident Video Retrieval (FIVR) using video similarity learning methods. FIVR is a video retrieval task that aims to retrieve all videos that depict the same incident given a query video { related video retrieval tasks adopt either very narrow or very broad scopes, considering only nearduplicate or same event videos. To formulate the case of same incident videos, we de ne three video associations taking into account the spatio-temporal spans captured by video pairs. To cover the benchmarking needs of FIVR, we construct a large-scale dataset, called FIVR-200K, consisting of 225,960 YouTube videos from major news events crawled from Wikipedia. The dataset contains four annotation labels according to FIVR de nitions; hence, it can simulate several retrieval scenarios with the same video corpus. To address FIVR, we propose two video-level approaches leveraging features extracted from intermediate layers of Convolutional Neural Networks (CNN). The rst is an unsupervised method that relies on a modi ed Bag-of-Word scheme, which generates video representations from the aggregation of the frame descriptors based on learned visual codebooks. The second is a supervised method based on Deep Metric Learning, which learns an embedding function that maps videos in a feature space where relevant video pairs are closer than the irrelevant ones. However, videolevel approaches generate global video representations, losing all spatial and temporal relations between compared videos. Therefore, we propose a video similarity learning approach that captures ne-grained relations between videos for accurate similarity calculation. We train a CNN architecture to compute video-to-video similarity from re ned frame-to-frame similarity matrices derived from a pairwise region-level similarity function. The proposed approaches have been extensively evaluated on FIVR- 200K and other large-scale datasets, demonstrating their superiority over other video retrieval methods and highlighting the challenging aspect of the FIVR problem