Cross-modal hashing is usually regarded as an effective technique for large-scale textual-visual cross retrieval, where data from different modalities are mapped into a shared Hamming space for matching. Most of the traditional textual-visual binary encoding methods only consider holistic image representations and fail to model descriptive sentences. This renders existing methods inappropriate to handle the rich semantics of informative cross-modal data for quality textual-visual search tasks. To address the problem of hashing cross-modal data with semantic-rich cues, in this paper, a novel integrated deep architecture is developed to effectively encode the detailed semantics of informative images and long descriptive sentences, named as Textual-Visual Deep Binaries (TVDB). In particular, region-based convolutional networks with long short-term memory units are introduced to fully explore image regional details while semantic cues of sentences are modeled by a text convolutional network. Additionally, we propose a stochastic batch-wise training routine, where high-quality binary codes and deep encoding functions are efficiently optimized in an alternating manner. Experiments are conducted on three multimedia datasets, i.e. Microsoft COCO, IAPR TC-12, and INRIA Web Queries, where the proposed TVDB model significantly outperforms state-of-the-art binary coding methods in the task of cross-modal retrieval.
The cutting-edge studies in vision and language achieve promising results in terms of visual question answering @cite_3 @cite_64 @cite_68 , caption generation @cite_18 @cite_27 @cite_38 , and real-valued cross-modal retrieval @cite_26 @cite_12 @cite_44 @cite_55 @cite_19 @cite_58 @cite_60 @cite_65 @cite_63 @cite_28 @cite_11 . The best-performing real-valued cross-modal retrieval models typically rely on densely annotated image-region and text pairs for embedding. However, these methods are far from satisfactory for large-scale data retrieval due to the inefficient similarity computation of real-valued embeddings.
@cite_38 @cite_18 @cite_64 @cite_26 @cite_60 @cite_28 @cite_55 @cite_58 @cite_65 @cite_3 @cite_44 @cite_19 @cite_27 @cite_63 @cite_68 @cite_12 @cite_11
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Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset. Cross-modal retrieval is a very hot research topic that is imperative to many applications involving multi-modal data. Discovering an appropriate representation for multi-modal data and learning a ranking function are essential to boost the cross-media retrieval. Motivated by the assumption that a compositional cross-modal semantic representation (pairs of images and text) is more attractive for cross-modal ranking, this paper exploits the existing image-text databases to optimize a ranking function for cross-modal retrieval, called deep compositional cross-modal learning to rank (C2MLR). In this paper, C2MLR considers learning a multi-modal embedding from the perspective of optimizing a pairwise ranking problem while enhancing both local alignment and global alignment. In particular, the local alignment (i.e., the alignment of visual objects and textual words) and the global alignment (i.e., the image-level and sentence-level alignment) are collaboratively utilized to learn the multi-modal embedding common space in a max-margin learning to rank manner. The experiments demonstrate the superiority of our proposed C2MLR due to its nature of multi-modal compositional embedding. The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities. Two hypotheses are then investigated regarding the fundamental attributes of these spaces. The first is that low-level cross-modal correlations should be accounted for. The second is that the space should enable semantic abstraction. Three new solutions to the cross-modal retrieval problem are then derived from these hypotheses: correlation matching (CM), an unsupervised method which models cross-modal correlations, semantic matching (SM), a supervised technique that relies on semantic representation, and semantic correlation matching (SCM), which combines both. An extensive evaluation of retrieval performance is conducted to test the validity of the hypotheses. All approaches are shown successful for text retrieval in response to image queries and vice versa. It is concluded that both hypotheses hold, in a complementary form, although evidence in favor of the abstraction hypothesis is stronger than that for correlation. Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of cross-modal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.