Journal Information
Image and Vision Computing (IVC)
Impact Factor:

Call For Papers
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.

In addition to regular manuscripts, Image and Vision Computing Journal solicits manuscripts for the Opinions Column, aimed at initiating a free forum for vision researchers to express their opinions on past, current, or future successes and challenges in research and the community.

An opinion paper should be succinct and focused on a particular topic. Addressing multiple related topics is also possible if this helps making the point. While posing questions helps raising awareness about certain issues, ideally, an opinion paper should also suggest a concrete direction how to address the issues. Topics of interest include, but are not limited to:

    Comments on success and challenges in a (sub-) field of computer vision,
    Remarks on new frontiers in computer vision
    Observations on current practices and trends in research, and suggestions for overcoming unsatisfying aspects
    Observations on current practices and trends in the community regarding, e.g., reviewing process, organizing conferences, how journals are run, and suggestions for overcoming unsatisfying aspects
    Reviews of early seminal work that may have fallen out of fashion
    Summaries of the evolution of one's line of research
    Recommendations for educating new generations of vision researchers.
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Cross-Media Learning for Visual Question Answering (VQA)
Submission Date: 2020-08-31

Visual Question Answering (VQA) is a recent hot topic which involves multimedia analysis, computer vision (CV), natural language processing (NLP), and even a broad perspective of artificial intelligence, which has attracted a large amount of interest from the deep learning, CV, and NLP communities. The definition of this task is shown as follows: a VQA system takes a picture and a free, open-ended question in the form of natural language about the picture as input and takes the generation of a piece of answer in the form of natural language as the output. It is required that pictures and problems should be taken as input of a VQA system, and a piece of human language is required to be generated as output by integrating information of these two parts. For a specific picture, if we want that the machine can answer a specific question about the picture in natural language, we need to enable the machine to have certain understanding of the content of the picture, and the meaning and intention of the question, as well as relevant knowledge. VQA relates to AI technologies in multiple aspects: fine-grained recognition, object recognition, behavior recognition, and understanding of the text contained in the question (NLP). Because VQA is closely related to the content both in CV and NLP, a natural QA solution is integrating CNN with RNN, which are successfully used in CV and NLP, to construct a composite model. To sum up, VQA is a learning task linked to CV and NLP. The task of VQA is rather challenging because it requires to comprehend textual questions, and analyze visual questions and image elements, as well as reasoning about these forms. Moreover, sometimes external or commonsense knowledge is required as the basis. Although some achievements have been made in VQA study currently, the overall accuracy rate is not high as far as the effect achieved by the current model is concerned. As the present VQA model is relatively simple in structure, single in the content and form of the answer, the correct answer is not so easy to obtain for the slightly complex questions which requires more prior knowledge for simple reasoning. Therefore, this Special Section in Journal of Image and Vision Computing aims to solicit original technical papers with novel contributions on the convergence of CV, NLP and Deep Leaning, as well as theoretical contributions that are relevant to the connection between natural language and CV. The topics of interest include, but are not limited to: Deep learning methodology and its applications on VQA, e.g. human computer interaction, intelligent cross-media query and etc. Image captioning indexing and retrieval Deep Learning for big data discovery Visual Relationship in VQA Question Answering in Images Grounding Language and VQA Image target location using VQA Captioning Events in Videos Attention mechanism in VQA system Exploring novel models and datasets for VQA
Last updated by Dou Sun in 2020-03-02
Special Issue on Deep Cross-Media Neural Model for Generating Image Descriptions
Submission Date: 2020-09-10

Understanding and generating image descriptions (UGID) are hot topics that combines the computer vision (CV) and natural language processing (NLP). UGID has broad application prospects in many fields of AI. Different from coarse-grained image understanding of independent labeling, the image description task needs to learn the natural language descriptions of images. This requires not only the model to recognize the objects in the image, but also other visual elements (e.g., actions and attributes of objects), but also understand the interrelationships between objects and generate human-readable description sentences, which is challenging. The real image understanding is to describe image with natural language and let the machine emulate humans for better human-computer interaction. With the fast development of deep learning in the fields of CV and NLP, the encoder-decoder based deep neural models have obtained breakthrough results in generating image descriptions in cross-media domains. As such, the image understanding may become a reality in future. However, current models can only provide a simple description about image, i.e., the number of descriptive words is usually limited and even the sentences are logically wrong. In this special issue, we invite the original contributions from diverse research fields, developing new deep cross-media neural model for understanding and generating image descriptions, which aims to reduce the gap between image understanding and natural language descriptions. The topics of interest include, but are not limited to: Attention guided UGID Visual relationship in UGID Compositional architectures for UGID Multimodal learning for UGID Describing novel objects in UGID Natural language processing model New datasets for UGID Novel encoder-decoder based architecture Deep cross-media neural model with applications of UGID, e.g., early childhood education, medical image analysis, assisted blinding and news automation, etc.
Last updated by Dou Sun in 2020-05-27
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