We introduce modifications to state-of-the-art approaches to aggregating local video descriptors by using attention mechanisms and function approximations. Rather than using ensembles of existing architectures, we provide an insight on creating new architectures. We demonstrate our solutions in the “The 2nd YouTube-8M Video Understanding Challenge”, by using frame-level video and audio descriptors. We obtain testing accuracy similar to the state of the art, while meeting budget constraints, and touch upon strategies to improve the state of the art. Model implementations are available in https: github.com pomonam LearnablePoolingMethods.
Despite the success and prominence of VLAD, numerous design choices in @cite_12 are left unexplained. For instance, the cluster similarities, @math , are computed via a simple linear transformation, where each local video descriptor is compared via dot product with a key per cluster centre, followed by a softmax layer. It is a straightforward idea to consider multiple keys per cluster centre, or to use multiple temporally close local descriptors per cluster similarity prediction.
@cite_12
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Recent works on image retrieval have proposed to index images by compact representations encoding powerful local descriptors, such as the closely related VLAD and Fisher vector. By combining such a representation with a suitable coding technique, it is possible to encode an image in a few dozen bytes while achieving excellent retrieval results. This paper revisits some assumptions proposed in this context regarding the handling of "visual burstiness", and shows that ad-hoc choices are implicitly done which are not desirable. Focusing on VLAD without loss of generality, we propose to modify several steps of the original design. Albeit simple, these modifications significantly improve VLAD and make it compare favorably against the state of the art. Encoding is one of the key factors for building an effective video representation. In the recent works, super vector-based encoding approaches are highlighted as one of the most powerful representation generators. Vector of Locally Aggregated Descriptors (VLAD) is one of the most widely used super vector methods. However, one of the limitations of VLAD encoding is the lack of spatial information captured from the data. This is critical, especially when dealing with video information. In this work, we propose Spatio-temporal VLAD (ST-VLAD), an extended encoding method which incorporates spatio-temporal information within the encoding process. This is carried out by proposing a video division and extracting specific information over the feature group of each video split. Experimental validation is performed using both hand-crafted and deep features. Our pipeline for action recognition with the proposed encoding method obtains state-of-the-art performance over three challenging datasets: HMDB51 (67.6 ), UCF50 (97.8 ) and UCF101 (91.5 ). The objective of this paper is large scale object instance retrieval, given a query image. A starting point of such systems is feature detection and description, for example using SIFT. The focus of this paper, however, is towards very large scale retrieval where, due to storage requirements, very compact image descriptors are required and no information about the original SIFT descriptors can be accessed directly at run time. We start from VLAD, the state-of-the art compact descriptor introduced by for this purpose, and make three novel contributions: first, we show that a simple change to the normalization method significantly improves retrieval performance, second, we show that vocabulary adaptation can substantially alleviate problems caused when images are added to the dataset after initial vocabulary learning. These two methods set a new state-of-the-art over all benchmarks investigated here for both mid-dimensional (20k-D to 30k-D) and small (128-D) descriptors. Our third contribution is a multiple spatial VLAD representation, MultiVLAD, that allows the retrieval and localization of objects that only extend over a small part of an image (again without requiring use of the original image SIFT descriptors). We consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector. Popular representations of this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When two such image representations are compared with the dot-product, the image-to-image similarity can be interpreted as a match kernel. In match kernels, one has to deal with interference , i.e., with the fact that even if two descriptors are unrelated, their matching score may contribute to the overall similarity. We formalise this problem and propose two related solutions, both aimed at equalising the individual contributions of the local descriptors in the final representation. These methods modify the aggregation stage by including a set of per-descriptor weights. They differ by the objective function that is optimised to compute those weights. The first is a “democratisation” strategy that aims at equalising the relative importance of each descriptor in the set comparison metric. The second one involves equalising the match of a single descriptor to the aggregated vector. These concurrent methods give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.