This paper targets at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset -- FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos.
@cite_38: While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadratically-constrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points; moreover, the sequential minimal optimization (SMO) techniques that are essential in large-scale implementations of the SVM cannot be applied because the cost function is non-differentiable. We propose a novel dual formulation of the QCQP as a second-order cone programming problem, and show how to exploit the technique of Moreau-Yosida regularization to yield a formulation to which SMO techniques can be applied. We present experimental results that show that our SMO-based algorithm is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.
@cite_60: We investigate the challenging issue of joint audio-visual analysis of generic videos targeting at semantic concept detection. We propose to extract a novel representation, the Short-term Audio-Visual Atom (S-AVA), for improved concept detection. An S-AVA is defined as a short-term region track associated with regional visual features and background audio features. An effective algorithm, named Short-Term Region tracking with joint Point Tracking and Region Segmentation (STR-PTRS), is developed to extract S-AVAs from generic videos under challenging conditions such as uneven lighting, clutter, occlusions, and complicated motions of both objects and camera. Discriminative audio-visual codebooks are constructed on top of S-AVAs using Multiple Instance Learning. Codebook-based features are generated for semantic concept detection. We extensively evaluate our algorithm over Kodak's consumer benchmark video set from real users. Experimental results confirm significant performance improvements - over 120 MAP gain compared to alternative approaches using static region segmentation without temporal tracking. The joint audio-visual features also outperform visual features alone by an average of 8.5 (in terms of AP) over 21 concepts, with many concepts achieving more than 20 .
@cite_0: In this paper, we propose a rank minimization method to fuse the predicted confidence scores of multiple models, each of which is obtained based on a certain kind of feature. Specifically, we convert each confidence score vector obtained from one model into a pairwise relationship matrix, in which each entry characterizes the comparative relationship of scores of two test samples. Our hypothesis is that the relative score relations are consistent among component models up to certain sparse deviations, despite the large variations that may exist in the absolute values of the raw scores. Then we formulate the score fusion problem as seeking a shared rank-2 pairwise relationship matrix based on which each original score matrix from individual model can be decomposed into the common rank-2 matrix and sparse deviation errors. A robust score vector is then extracted to fit the recovered low rank score relation matrix. We formulate the problem as a nuclear norm and l 1 norm optimization objective function and employ the Augmented Lagrange Multiplier (ALM) method for the optimization. Our method is isotonic (i.e., scale invariant) to the numeric scales of the scores originated from different models. We experimentally show that the proposed method achieves significant performance gains on various tasks including object categorization and video event detection.
@cite_59: Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams.
@cite_15: Late fusion addresses the problem of combining the prediction scores of multiple classifiers, in which each score is predicted by a classifier trained with a specific feature. However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples. In this paper, we propose a sample-specific late fusion method to address this issue. Specifically, we cast the problem into an information propagation process which propagates the fusion weights learned on the labeled samples to individual unlabeled samples, while enforcing that positive samples have higher fusion scores than negative samples. In this process, we identify the optimal fusion weights for each sample and push positive samples to top positions in the fusion score rank list. We formulate our problem as a L∞ norm constrained optimization problem and apply the Alternating Direction Method of Multipliers for the optimization. Extensive experiment results on various visual categorization tasks show that the proposed method consistently and significantly beats the state-of-the-art late fusion methods. To the best knowledge, this is the first method supporting sample-specific fusion weight learning.
@cite_17: With the recent efforts made by computer vision researchers, more and more types of features have been designed to describe various aspects of visual characteristics. Modeling such heterogeneous features has become an increasingly critical issue. In this paper, we propose a machinery called the Heterogeneous Feature Machine (HFM) to effectively solve visual recognition tasks in need of multiple types of features. Our HFM builds a kernel logistic regression model based on similarities that combine different features and distance metrics. Different from existing approaches that use a linear weighting scheme to combine different features, HFM does not require the weights to remain the same across different samples, and therefore can effectively handle features of different types with different metrics. To prevent the model from overfitting, we employ the so-called group LASSO constraints to reduce model complexity. In addition, we propose a fast algorithm based on co-ordinate gradient descent to efficiently train a HFM. The power of the proposed scheme is demonstrated across a wide variety of visual recognition tasks including scene, event and action recognition.
In video categorization systems, two types of feature fusion strategies are widely used, , the early fusion and the late fusion. Multiple kernel learning @cite_38 was utilized to estimate fusion weights @cite_17 @cite_59 , which are needed in both early fusion and late fusion. To efficiently exploit the relationships of features, several more advanced feature fusion techniques were conducted. An optimization framework in @cite_0 applied a shared low-rank matrix to reduce noises in the fusion. An audio-visual joint codebook proposed by Jiang @cite_60 discovered and fused the correlations of audio and visual features for video classification. The dynamic fusion is utilized in @cite_15 as the best feature combination strategy.