There has been much recent work on image captioning models that describe the factual aspects of an image. Recently, some models have incorporated non-factual aspects into the captions, such as sentiment or style. However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption. To address this, we design an attention-based model to better add sentiment to image captions. The model embeds and learns sentiment with respect to image-caption data, and uses both high-level and word-level sentiment information during the learning process. The model outperforms the state-of-the-art work in image captioning with sentiment using standard evaluation metrics. An analysis of generated captions also shows that our model does this by a better selection of the sentiment-bearing adjectives and adjective-noun pairs.
Yu al @cite_25 and You al @cite_29 applied a notion of Semantic attention to detected visual attributes, which is learned in an end-to-end fashion. This attention model is used to attend to semantic concepts detected from various parts of a given image. Here, they used the visual content only in the initial time step. In other time steps, Semantic attention was used to select the extracted semantic concepts. That is, Semantic attention differs from spatial attention, which attends to spatial features in every time step, and does not preserve the spatial information of the detected concepts.
@cite_29 @cite_25
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In this paper, we study the task of detecting semantic parts of an object. This is very important in computer vision, as it provides the possibility to parse an object as human do, and helps us better understand object detection algorithms. Also, detecting semantic parts is very challenging especially when the parts are partially or fully occluded. In this scenario, the popular proposal-based methods like Faster-RCNN often produce unsatisfactory results, because both the proposal extraction and classification stages may be confused by the irrelevant occluders. To this end, we propose a novel detection framework, named DeepVoting, which accumulates local visual cues, called visual concepts (VC), to locate the semantic parts. Our approach involves adding two layers after the intermediate outputs of a deep neural network. The first layer is used to extract VC responses, and the second layer performs a voting mechanism to capture the spatial relationship between VC's and semantic parts. The benefit is that each semantic part is supported by multiple VC's. Even if some of the supporting VC's are missing due to occlusion, we can still infer the presence of the target semantic part using the remaining ones. To avoid generating an exponentially large training set to cover all occlusion cases, we train our model without seeing occlusion and transfer the learned knowledge to deal with occlusions. This setting favors learning the models which are naturally robust and adaptive to occlusions instead of over-fitting the occlusion patterns in the training data. In experiments, DeepVoting shows significantly better performance on semantic part detection in occlusion scenarios, compared with Faster-RCNN, with one order of magnitude fewer parameters and 2.5x testing speed. In addition, DeepVoting is explainable as the detection result can be diagnosed via looking up the voted VC's. Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr9k, Flickr30k and MS COCO. Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO. In online education systems, finding similar exercises is a fundamental task of many applications, such as exercise retrieval and student modeling. Several approaches have been proposed for this task by simply using the specific textual content (e.g. the same knowledge concepts or the similar words) in exercises. However, the problem of how to systematically exploit the rich semantic information embedded in multiple heterogenous data (e.g. texts and images) to precisely retrieve similar exercises remains pretty much open. To this end, in this paper, we develop a novel Multimodal Attention-based Neural Network (MANN) framework for finding similar exercises in large-scale online education systems by learning a unified semantic representation from the heterogenous data. In MANN, given exercises with texts, images and knowledge concepts, we first apply a convolutional neural network to extract image representations and use an embedding layer for representing concepts. Then, we design an attention-based long short-term memory network to learn a unified semantic representation of each exercise in a multimodal way. Here, two attention strategies are proposed to capture the associations of texts and images, texts and knowledge concepts, respectively. Moreover, with a Similarity Attention, the similar parts in each exercise pair are also measured. Finally, we develop a pairwise training strategy for returning similar exercises. Extensive experimental results on real-world data clearly validate the effectiveness and the interpretation power of MANN.