The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.
The proposed LM3FE belongs to multi-modal subspace learning but utilizes a weighted modality combination training strategy. Recently, a multi-modal feature selection method @cite_34 has been proposed that explores the correlations between different modalities by taking the tensor product of their feature spaces. However, this method cannot handle the multi-class problem naturally and must train an SVM classifier to eliminate one feature at a time. The relationships between different classes are therefore discarded, and the training cost is very high. The closest work to our method is the multi-modal feature learning approach presented in @cite_25 since it also utilizes the @math -norm to discover the task relationships, but it differs from our method in that the group @math -norm is employed to capture the correlations between modalities. The main drawback of this approach is that the feature weight matrices of different modalities are concatenated and directly utilized as the prediction matrix. Also, least squares loss is adopted, therefore the prediction (e.g., classification) power of the learned features is limited. In the proposed LM3FE, a prediction matrix is learned in addition to the feature extraction matrices, and strongly predictive features are obtained by minimizing the hinge loss under the maximum margin principle.
@cite_34 @cite_25
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This paper proposes a novel deep framework of multi-networks joint learning for large-scale cross-modal retrieval. For most existing cross-modal methods, the processes of training and testing don't care about the problem of memory requirement. Hence, they are generally implemented on small-scale data. Moreover, they take feature learning and latent space embedding as two separate steps which cannot generate specific features to accord with the cross-modal task. To alleviate the problems, we first disintegrate the multiplication and inverse of some big matrices, usually involved in existing methods, into that of many sub-matrices. Each sub-matrix is targeted to dispose one pair of image-sentence, for which we further design a novel sampling strategy to select the most representative samples to construct the cross-modal ranking loss and within-modal discriminant loss functions. By this way, the proposed model consumes less memory each time such that it can scale to large-scale data. Furthermore, we apply the proposed discriminative ranking loss to effectively unify two heterogenous networks, deep residual network for images and long short-term memory for sentences, into an end-to-end deep learning architecture. Finally, we can simultaneously achieve specific features adapting to cross-modal task and learn a shared latent space for images and sentences. Extensive evaluations on two large-scale cross-modal datasets show that the proposed method brings substantial improvements over other state-of-the-art ranking methods. Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin of the learned deep representations. To simultaneously achieve these two goals, different solutions have been proposed in the literature, such as the pairwise or triplet losses. However, these carry the extra task of selecting pairs or triplets, and the extra computational burden of computing and learning for many combinations of them. In this paper, we propose a plug-and-play loss term for deep networks that explicitly reduces intra-class variance and enforces inter-class margin simultaneously, in a simple and elegant geometric manner. For each class, the deep features are collapsed into a learned linear subspace, or union of them, and inter-class subspaces are pushed to be as orthogonal as possible. Our proposed Orthogonal Low-rank Embedding (OLA‰) does not require carefully crafting pairs or triplets of samples for training, and works standalone as a classification loss, being the first reported deep metric learning framework of its kind. Because of the improved margin between features of different classes, the resulting deep networks generalize better, are more discriminative, and more robust. We demonstrate improved classification performance in general object recognition, plugging the proposed loss term into existing off-the-shelf architectures. In particular, we show the advantage of the proposed loss in the small data model scenario, and we significantly advance the state-of-the-art on the Stanford STL-10 benchmark. In this paper, we study the problem of training large-scale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very limited training samples, by introducing a regularizer to the cross entropy loss for the multi-nomial logistic regression (MLR) learning. This regularizer encourages the directions of the face features from the same class to be close to the direction of their corresponding classification weight vector in the logistic regression. Second, we build a multi-class classifier using MLR on top of the learned face feature extraction model. Since the standard MLR has poor generalization capability for the one-shot classes even if these classes have been oversampled, we propose a novel supervision signal called underrepresented-classes promotion loss, which aligns the norms of the weight vectors of the one-shot classes (a.k.a. underrepresented-classes) to those of the normal classes. In addition to the original cross entropy loss, this new loss term effectively promotes the underrepresented classes in the learned model and leads to a remarkable improvement in face recognition performance. We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our solution recognizes 94.89 of the test images at the precision of 99 for the one-shot classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017. A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognition task, it cooperatively trains a single convolutional neural network (named c-ConvNet) on both RGB visual features and depth features, and deeply aggregates the two kinds of features for action recognition. Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities. The ranking loss consists of intra-modality and cross-modality triplet losses, and it reduces both the intra-modality and cross-modality feature variations. Furthermore, the correlations between RGB and depth data are embedded in the c-ConvNet, and can be retrieved by either of the modalities and contribute to the recognition in the case even only one of the modalities is available. The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results.