922 research outputs found
Unpaired Image Captioning via Scene Graph Alignments
Most of current image captioning models heavily rely on paired image-caption
datasets. However, getting large scale image-caption paired data is
labor-intensive and time-consuming. In this paper, we present a scene
graph-based approach for unpaired image captioning. Our framework comprises an
image scene graph generator, a sentence scene graph generator, a scene graph
encoder, and a sentence decoder. Specifically, we first train the scene graph
encoder and the sentence decoder on the text modality. To align the scene
graphs between images and sentences, we propose an unsupervised feature
alignment method that maps the scene graph features from the image to the
sentence modality. Experimental results show that our proposed model can
generate quite promising results without using any image-caption training
pairs, outperforming existing methods by a wide margin.Comment: Accepted in ICCV 201
Consensus Graph Representation Learning for Better Grounded Image Captioning
The contemporary visual captioning models frequently hallucinate objects that
are not actually in a scene, due to the visual misclassification or
over-reliance on priors that resulting in the semantic inconsistency between
the visual information and the target lexical words. The most common way is to
encourage the captioning model to dynamically link generated object words or
phrases to appropriate regions of the image, i.e., the grounded image
captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects)
that has not solved the key issue of object hallucination, i.e., the semantic
inconsistency. In this paper, we take a novel perspective on the issue above -
exploiting the semantic coherency between the visual and language modalities.
Specifically, we propose the Consensus Rraph Representation Learning framework
(CGRL) for GIC that incorporates a consensus representation into the grounded
captioning pipeline. The consensus is learned by aligning the visual graph
(e.g., scene graph) to the language graph that consider both the nodes and
edges in a graph. With the aligned consensus, the captioning model can capture
both the correct linguistic characteristics and visual relevance, and then
grounding appropriate image regions further. We validate the effectiveness of
our model, with a significant decline in object hallucination (-9% CHAIRi) on
the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several
automatic metrics and human evaluation, the results indicate that the proposed
approach can simultaneously improve the performance of image captioning (+2.9
Cider) and grounding (+2.3 F1LOC).Comment: 9 pages, 5 figures, AAAI 202
Text to image synthesis for improved image captioning
Generating textual descriptions of images has been an important topic in computer vision and natural language processing. A number of techniques based on deep learning have been proposed on this topic. These techniques use human-annotated images for training and testing the models. These models require a large number of training data to perform at their full potential. Collecting human generated images with associative captions is expensive and time-consuming. In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model. We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images. We use an attention-based image captioning method trained on both real and synthetic images to generate the captions. We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics. We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training
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