4,259 research outputs found
Variational recurrent sequence-to-sequence retrieval for stepwise illustration
We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods
Multi-modal gated recurrent units for image description
Using a natural language sentence to describe the content of an image is a
challenging but very important task. It is challenging because a description
must not only capture objects contained in the image and the relationships
among them, but also be relevant and grammatically correct. In this paper a
multi-modal embedding model based on gated recurrent units (GRU) which can
generate variable-length description for a given image. In the training step,
we apply the convolutional neural network (CNN) to extract the image feature.
Then the feature is imported into the multi-modal GRU as well as the
corresponding sentence representations. The multi-modal GRU learns the
inter-modal relations between image and sentence. And in the testing step, when
an image is imported to our multi-modal GRU model, a sentence which describes
the image content is generated. The experimental results demonstrate that our
multi-modal GRU model obtains the state-of-the-art performance on Flickr8K,
Flickr30K and MS COCO datasets.Comment: 25 pages, 7 figures, 6 tables, magazin
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows
both text and sketch as input modalities for the query. A cross-modal deep
network architecture is formulated to jointly model the sketch and text input
modalities as well as the the image output modality, learning a common
embedding between text and images and between sketches and images. In addition,
an attention model is used to selectively focus the attention on the different
objects of the image, allowing for retrieval with multiple objects in the
query. Experiments show that the proposed method performs the best in both
single and multiple object image retrieval in standard datasets.Comment: Accepted at ICPR 201
Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text
Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, with possible malicious intent. It is, therefore, important to
develop methods to assess or verify the integrity of these multimedia packages.
Using computer vision and natural language processing methods to directly
compare the image (or video) and the associated caption to verify the integrity
of a media package is only possible for a limited set of objects and scenes. In
this paper, we present a novel deep learning-based approach for assessing the
semantic integrity of multimedia packages containing images and captions, using
a reference set of multimedia packages. We construct a joint embedding of
images and captions with deep multimodal representation learning on the
reference dataset in a framework that also provides image-caption consistency
scores (ICCSs). The integrity of query media packages is assessed as the
inlierness of the query ICCSs with respect to the reference dataset. We present
the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media
packages from Flickr, which we make available to the research community. We use
both the newly created dataset as well as Flickr30K and MS COCO datasets to
quantitatively evaluate our proposed approach. The reference dataset does not
contain unmanipulated versions of tampered query packages. Our method is able
to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO,
respectively, for detecting semantically incoherent media packages.Comment: *Ayush Jaiswal and Ekraam Sabir contributed equally to the work in
this pape
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