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.
As tangent encoding is a technique to linearly approximate a high dimensional function @cite_17 , the authors naturally extend VLAD to a second order approximation to obtain a unique local descriptor embedding, while incorporating ideas from other methods such as @cite_9 for aggregation. An embedding per local descriptor is described in Equation , where @math is the concatenation of three vectors, per cluster center j. The @math can be thought of as cluster similarities, as in Equation .
@cite_9 @cite_17
2567033548 2058113271 2105516263 2084363474
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 ). In recent years, VLAD has become a popular method which encoding powerful local descriptors to the compact representations. By using this approach, an image can be represented by just a few dozen bytes while preserving excellent retrieval results after the dimensionality reduction and compression. However, throwing away the spatial information is one of the biggest weaknesses of VLAD. This paper adopts the spatial pyramid pooling method to incorporate the spatial information into the VLAD vectors. Furthermore, a new normalization method is proposed to hold this advantage. By the proposed method, the performance of VLAD can be boosted through combining spatial information. The experimental results show that our approach outperforms VLAD in almost all configurations. 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. This paper addresses the problem of learning similarity-preserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient alternating minimization scheme for finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube. This method, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). Our experiments show that the resulting binary coding schemes decisively outperform several other state-of-the-art methods.