Our objective is video retrieval based on natural language queries. In addition, we consider the analogous problem of retrieving sentences or generating descriptions given an input video. Recent work has addressed the problem by embedding visual and textual inputs into a common space where semantic similarities correlate to distances. We also adopt the embedding approach, and make the following contributions: First, we utilize web image search in sentence embedding process to disambiguate fine-grained visual concepts. Second, we propose embedding models for sentence, image, and video inputs whose parameters are learned simultaneously. Finally, we show how the proposed model can be applied to description generation. Overall, we observe a clear improvement over the state-of-the-art methods in the video and sentence retrieval tasks. In description generation, the performance level is comparable to the current state-of-the-art, although our embeddings were trained for the retrieval tasks.
Representation learning using deep neural networks is explored in many tasks @cite_16 @cite_8 @cite_30 @cite_32 @cite_18 @cite_11 . Frome al @cite_30 proposed image classification by computing similarity between joint representations of images and labels, and Zhu al @cite_11 addressed alignment of a movie and sentences in a book using joint representations for video clips and sentences. Their approach also computes similarity between sentences and subtitles of video clips to improve the alignment of video clips and sentences.
@cite_30 @cite_18 @cite_8 @cite_32 @cite_16 @cite_11
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Abstract With the novel and fast advances in the area of deep neural networks, several challenging image-based tasks have been recently approached by researchers in pattern recognition and computer vision. In this paper, we address one of these tasks, which is to match image content with natural language descriptions, sometimes referred as multimodal content retrieval. Such a task is particularly challenging considering that we must find a semantic correspondence between captions and the respective image, a challenge for both computer vision and natural language processing areas. For such, we propose a novel multimodal approach based solely on convolutional neural networks for aligning images with their captions by directly convolving raw characters. Our proposed character-based textual embeddings allow the replacement of both word-embeddings and recurrent neural networks for text understanding, saving processing time and requiring fewer learnable parameters. Our method is based on the idea of projecting both visual and textual information into a common embedding space. For training such embeddings we optimize a contrastive loss function that is computed to minimize order-violations between images and their respective descriptions. We achieve state-of-the-art performance in the most well-known image-text alignment datasets, namely Microsoft COCO, Flickr8k, and Flickr30k, with a method that is conceptually much simpler and that possesses considerably fewer parameters than current approaches. Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts. Learning rich visual representations often require training on datasets of millions of manually annotated examples. This substantially limits the scalability of learning effective representations as labeled data is expensive or scarce. In this paper, we address the problem of unsupervised visual representation learning from a large, unlabeled collection of images. By representing each image as a node and each nearest-neighbor matching pair as an edge, our key idea is to leverage graph-based analysis to discover positive and negative image pairs (i.e., pairs belonging to the same and different visual categories). Specifically, we propose to use a cycle consistency criterion for mining positive pairs and geodesic distance in the graph for hard negative mining. We show that the mined positive and negative image pairs can provide accurate supervisory signals for learning effective representations using Convolutional Neural Networks (CNNs). We demonstrate the effectiveness of the proposed unsupervised constraint mining method in two settings: (1) unsupervised feature learning and (2) semi-supervised learning. For unsupervised feature learning, we obtain competitive performance with several state-of-the-art approaches on the PASCAL VOC 2007 dataset. For semi-supervised learning, we show boosted performance by incorporating the mined constraints on three image classification datasets. Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method.