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Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas – fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community. [ABSTRACT FROM AUTHOR]

With the rapid growth of multimedia data on the Internet, there has been a rapid rise in the demand for visual–textual cross-media retrieval between images and sentences. However, the heterogeneous property of visual and textual data brings huge challenges to measure the cross-media similarity for retrieval. Although existing methods have achieved great progress with the strong learning ability of the deep neural network, they rely heavily on the scale of training data with manual annotation, that is, either pairwise image–sentence annotation or category annotation as supervised information for visual–textual correlation learning, which are extremely labor and time consuming to collect. Without any pairwise or category annotation, it is highly challenging to construct a correlation between images and sentences due to their inconsistent distributions and representations. But people can naturally understand the correlation between visual and textual data in high-level semantic, and those images and sentences containing the same group of semantic concepts can be easily matched in human brain. Inspired by the above human cognitive process, this article proposes an unsupervised visual–textual correlation learning (UVCL) approach to construct correlations without any manual annotation. The contributions are summarized as follows: 1) unsupervised semantic-guided cross-media correlation mining is proposed to bridge the heterogeneous gap between visual and textual data. We measure the semantic matching degree between images and sentences, and generate descriptive sentences according to the concepts extracted from images to further augment the training data in an unsupervised manner. Therefore, the approach can exploit the semantic knowledge within both visual and textual data to reduce the gap between them for further correlation learning and 2) unsupervised visual–textual fine-grained semantic alignment is proposed to learn cross-media correlation by aligning the fine-grained visual local patches and textual keywords with fine-grained soft attention as well as semantic-guided hard attention, and the results can effectively highlight the fine-grained semantic information within both images and sentences to boost visual–textual alignment. Extensive experiments are conducted to perform visual–textual cross-media retrieval in unsupervised setting without any manual annotation on two widely used datasets, namely, Flickr-30K and MS-COCO, which verify the effectiveness of our proposed UVCL approach. [ABSTRACT FROM AUTHOR]

Reasoning is one of the central topics in artificial intelligence. As an important reasoning paradigm, entailment recognition has attracted much research interest, which judges if a hypothesis can be inferred from given premises. However, existing research mainly focuses on text-based analysis, that is, recognizing textual entailment (RTE), which limits its depth and width. Actually, the knowledge and inference process of human are across different sensory organs like language and vision, with unique perspectives to represent complementary reasoning cues. It is significant to extend existing entailment recognition research to cross-media scenarios, that is, recognizing cross-media entailment (RCE). Therefore, this article focuses on one representative RCE task: visual–textual reasoning, and proposes the visual–textual hybrid sequence matching (VHSM) approach. VHSM can reason from image-text premises to text hypotheses, whose contributions are: 1) visual–textual hybrid multicontext inference is proposed to address RCE via matching with hybrid context embeddings, along with adaptive gated aggregation to obtain the final prediction results. It can fully exploit complementary visual–textual cue interaction during joint reasoning; 2) memory attention-based context embedding is proposed to sequentially encode hybrid context embeddings, with the memory attention networks to compare neighboring time–steps. This can capture the important memory dimensions by coefficient assignment, which fully exploits the visual–textual context correlation; and 3) cross-task and visual–textual transfer strategy is further proposed to enrich correlation training information for boosting reasoning accuracy, which transfers knowledge not only from cross-media retrieval task to RCE but also between corresponding text and image premises. The experimental results of recognizing visual–textual entailment task on the SNLI dataset verify the effectiveness of VHSM. [ABSTRACT FROM AUTHOR]

When glancing at an image, human can infer what is hidden in the image beyond what is visually obvious, such as objects’ functions, people’s intents and mental states. However, such a visual reasoning paradigm is tremendously difficult for computer, requiring knowledge about how the world works. To address this issue, we propose Commonsense Knowledge based Reasoning Model (CKRM) to acquire external knowledge to support Visual Commonsense Reasoning (VCR) task, where the computer is expected to answer challenging visual questions. Our key ideas are: (1) To bridge the gap between recognition-level and cognition-level image understanding, we inject external commonsense knowledge via multi-level knowledge transfer network, achieving cell-level, layer-level and attention-level joint information transfer. It can effectively capture knowledge from different perspectives, and perceive common sense of human in advance. (2) To further promote image understanding at cognitive level, we propose a knowledge based reasoning approach, which can relate the transferred knowledge to visual content and compose the reasoning cues to derive the final answer. Experiments are conducted on the challenging visual commonsense reasoning dataset VCR to verify the effectiveness of our proposed CKRM approach, which can significantly improve reasoning performance and achieve the state-of-the-art accuracy. [ABSTRACT FROM AUTHOR]

Existing cross-media retrieval methods are usually conducted under the supervised setting, which need lots of annotated training data. Generally, it is extremely labor-consuming to annotate cross-media data. So unsupervised cross-media retrieval is highly demanded, which is very challenging because it has to handle heterogeneous distributions across different media types without any annotated information. To address the above challenge, this paper proposes Domain Adaptation with Scene Graph (DASG) approach, which transfers knowledge from the source domain to improve cross-media retrieval in the target domain. Our DASG approach takes Visual Genome as the source domain, which contains image knowledge in the form of scene graph. The main contributions of this paper are as follows: First, we propose to address unsupervised cross-media retrieval by domain adaptation. Instead of using the labor-consuming annotated information of cross-media data in the training stage, our DASG approach learns cross-media correlation knowledge from Visual Genome, and then transfers the knowledge to cross-media retrieval through media alignment and distribution alignment. Second, our DASG approach utilizes fine-grained information via scene graph representation to enhance generalization capability across domains. The generated scene graph representation builds (subject $\rightarrow $ relationship $\rightarrow $ object) triplets by exploiting objects and relationships within image and text, which makes the cross-media correlation more precise and promotes unsupervised cross-media retrieval. Third, we exploit the related tasks including object and relationship detection for learning more discriminative features across domains. Leveraging the semantic information of objects and relationships improves cross-media correlation learning for retrieval. Experiments on two widely-used cross-media retrieval datasets, namely Flickr-30K and MS-COCO, show the effectiveness of our DASG approach. [ABSTRACT FROM AUTHOR]

Text-to-image synthesis is to generate images with the consistent content as the given text description, which is a highly challenging task with two main issues: visual reality and content consistency. Recently, it is available to generate images with high visual reality due to the significant progress of generative adversarial networks. However, translating text description to image with high content consistency is still ambitious. For addressing the above issues, it is reasonable to establish a transitional space with interpretable representation as a bridge to associate text and image. So we propose a text-to-image synthesis approach named Bridge-like Generative Adversarial Networks (Bridge-GAN). Its main contributions are: (1) A transitional space is established as a bridge for improving content consistency, where the interpretable representation can be learned by guaranteeing the key visual information from given text descriptions. (2) A ternary mutual information objective is designed for optimizing the transitional space and enhancing both the visual reality and content consistency. It is proposed under the goal to disentangle the latent factors conditioned on text description for further interpretable representation learning. Comprehensive experiments on two widely-used datasets verify the effectiveness of our Bridge-GAN with the best performance. [ABSTRACT FROM AUTHOR]

Text-to-image synthesis (T2IS) has drawn increasing interest recently, which can automatically generate images conditioned on text descriptions. It is a highly challenging task that learns a mapping from a semantic space of text description to a complex RGB pixel space of image. The main issues of T2IS lie in two aspects: semantic consistency and visual quality. The distributions between text descriptions and image contents are inconsistent since they belong to different modalities. So it is ambitious to generate images containing consistent semantic contents with the text descriptions, which is the semantic consistency issue. Moreover, due to the discrepancy of data distributions between real and synthetic images in huge pixel space, it is hard to approximate the real data distribution for synthesizing photo-realistic images, which is the visual quality issue. For addressing the above issues, we propose a cross-task knowledge distillation (CKD) approach to transfer knowledge from multiple image semantic understanding tasks into T2IS task. There is amount of knowledge in image semantic understanding tasks to translate image contents into semantic representation, which is advantageous to address the issues of semantic consistency and visual quality for T2IS. Moreover, we design a multi-stage knowledge distillation paradigm to decompose the distillation process into multiple stages. By this paradigm, it is effective to approximate the distributions of real image and understand textual information for T2IS, which can improve the visual quality and semantic consistency of synthetic images. Comprehensive experiments on widely-used datasets show the effectiveness of our proposed CKD approach. [ABSTRACT FROM AUTHOR]

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform cross-media retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning cross-media correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach. [ABSTRACT FROM AUTHOR]

The heterogeneity gap leads to inconsistent distributions and representations between image and text, which rises a challenging task to measure their similarities and construct cross-media correlation between them. The existing works mainly model the cross-media correlation in a common subspace, which causes insufficient correlation modeling in such third-party subspace with intermediate unidirectional transformation. Inspired by the recent advances of neural machine translation, which aims to establish a corresponding relationship between two entirely different languages, we can naturally discover that it has striking common characteristic with cross-media correlation learning to consider image and text as bilingual pairs, where the image is treated as a special kind of language to provide visual description, so that bidirectional transformation can be conducted between image and text to effectively explore cross-media correlation in the feature space of each media type. Thus, we propose a reinforced cross-media bidirectional translation (RCBT) approach to model the correlation between visual and textual descriptions. First, cross-media bidirectional translation mechanism is proposed to conduct direct transformation between the bilingual pairs of visual and textual descriptions bidirectionally, where the cross-media correlation can be effectively captured in both feature spaces of image and text through bidirectional translation training. Second, cross-media context-aware network with residual attention is proposed to exploit the rich spatial and temporal context hints with cross-media convolutional recurrent neural network, which can lead to more precise correlation learning for promoting bidirectional translation process. Third, cross-media reinforcement learning is proposed to perform a two-agent communication game played as a round between image and text to boost the bidirectional translation process, and we further extract inter-media and intra-media reward signals to provide complementary clues for learning cross-media correlation. Extensive experiments are conducted on cross-media retrieval to verify the effectiveness of our proposed RCBT approach, compared with 11 state-of-the-art methods on three cross-media datasets. [ABSTRACT FROM AUTHOR]

Fine-grained visual categorization is to recognize hundreds of subcategories belonging to the same basic-level category, which is a highly challenging task due to the quite subtle and local visual distinctions among similar subcategories. Most existing methods generally learn part detectors to discover discriminative regions for better categorization performance. However, not all parts are beneficial and indispensable for visual categorization, and the setting of part detector number heavily relies on prior knowledge as well as experimental validation. As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics and rarely pay attention to common characteristics as well as the background areas. This is an involuntary transfer from human visual attention to textual attention, which leads to the fact that textual attention tells us how many and which parts are discriminative and significant to categorization. So, textual attention could help us to discover visual attention in the image. Inspired by this, we propose a fine-grained visual-textual representation learning (VTRL) approach, and its main contributions are: 1) fine-grained visual-textual pattern mining devotes to discovering discriminative visual-textual pairwise information for boosting categorization performance through jointly modeling vision and text with generative adversarial networks, which automatically and adaptively discovers discriminative parts and 2) VTRL jointly combines visual and textual information, which preserves the intra-modality and inter-modality information to generate complementary fine-grained representation, as well as further improves categorization performance. Comprehensive experimental results on the widely used CUB-200-2011 and Oxford Flowers-102 datasets demonstrate the effectiveness of our VTRL approach, which achieves the best categorization accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challenge. Existing cross-media retrieval methods usually focus on modeling the pairwise correlation or semantic information separately. In fact, these two kinds of information are complementary to each other and optimizing them simultaneously can further improve the accuracy. In this paper, we propose a novel feature learning algorithm for cross-media data, called joint representation learning (JRL), which is able to explore jointly the correlation and semantic information in a unified optimization framework. JRL integrates the sparse and semisupervised regularization for different media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type. On one hand, JRL learns sparse projection matrix for different media simultaneously, so different media can align with each other, which is robust to the noise. On the other hand, both the labeled data and unlabeled data of different media types are explored. Unlabeled examples of different media types increase the diversity of training data and boost the performance of joint representation learning. Furthermore, JRL can not only reduce the dimension of the original features, but also incorporate the cross-media correlation into the final representation, which further improves the performance of both cross-media retrieval and single-media retrieval. Experiments on two datasets with up to five media types show the effectiveness of our proposed approach, as compared with the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

We propose a new method to refine the result of video annotation by exploiting the semantic and visual context of video. On one hand, semantic context mining is performed in a supervised way, using the manual concept labels of the training set. It is very useful for boosting video annotation performance, because semantic context is learned from labels given by people, indicating human intention. In this paper, we model the spatial and temporal context in video by using conditional random fields with different structures. Comparing with existing methods, our method could more accurately capture concept relationship in video and could more effectively improve the video annotation performance. On the other hand, visual context mining is performed in a semi-supervised way based on the visual similarities among video shots. It indicates the natural visual property of video, and could be considered as the compensation to semantic context, which generally could not be perfectly modeled. In this paper, we construct a graph based on the visual similarities among shots. Then a semi-supervised learning approach is adopt based on the graph to propagate probabilities of the reliable shots to others having similar visual features with them. Extensive experimental results on the widely used TRECVID datasets exhibit the effectiveness of our method for improving video annotation accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity between images. Specifically, we represent each image as a 2-D sequence of visual words and measure the similarity between two 2-D sequences using the shared occurrences of s-length 1-D subsequences by decomposing each 2-D sequence into two orthogonal 1-D sequences. Based on our proposed spatial string kernel, we further formulate automatic image annotation as a contextual keyword propagation problem, which can be solved very efficiently by linear programming. Unlike the traditional relevance models that treat each keyword independently, the proposed contextual kernel method for keyword propagation takes into account the semantic context of annotation keywords and propagates multiple keywords simultaneously. Significantly, this type of semantic context can also be incorporated into spectral embedding for refining the annotations of images predicted by keyword propagation. Experiments on three standard image datasets demonstrate that our contextual kernel and spectral methods can achieve significantly better results than the state of the art. [ABSTRACT FROM AUTHOR]