3,848 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
Unsupervised Cross-lingual Image Captioning
Most recent image captioning works are conducted in English as the majority
of image-caption datasets are in English. However, there are a large amount of
non-native English speakers worldwide. Generating image captions in different
languages is worth exploring. In this paper, we present a novel unsupervised
method to generate image captions without using any caption corpus. Our method
relies on 1) a cross-lingual auto-encoding, which learns the scene graph
mapping function along with the scene graph encoders and sentence decoders on
machine translation parallel corpora, and 2) an unsupervised feature mapping,
which seeks to map the encoded scene graph features from image modality to
sentence modality. By leveraging cross-lingual auto-encoding, cross-modal
feature mapping, and adversarial learning, our method can learn an image
captioner to generate captions in different languages. We verify the
effectiveness of our proposed method on the Chinese image caption generation.
The comparisons against several baseline methods demonstrate the effectiveness
of our approach.Comment: 8 page
Object-Centric Unsupervised Image Captioning
Image captioning is a longstanding problem in the field of computer vision
and natural language processing. To date, researchers have produced impressive
state-of-the-art performance in the age of deep learning. Most of these
state-of-the-art, however, requires large volume of annotated image-caption
pairs in order to train their models. When given an image dataset of interests,
practitioner needs to annotate the caption for each image in the training set
and this process needs to happen for each newly collected image dataset. In
this paper, we explore the task of unsupervised image captioning which utilizes
unpaired images and texts to train the model so that the texts can come from
different sources than the images. A main school of research on this topic that
has been shown to be effective is to construct pairs from the images and texts
in the training set according to their overlap of objects. Unlike in the
supervised setting, these constructed pairings are however not guaranteed to
have fully overlapping set of objects. Our work in this paper overcomes this by
harvesting objects corresponding to a given sentence from the training set,
even if they don't belong to the same image. When used as input to a
transformer, such mixture of objects enables larger if not full object
coverage, and when supervised by the corresponding sentence, produced results
that outperform current state of the art unsupervised methods by a significant
margin. Building upon this finding, we further show that (1) additional
information on relationship between objects and attributes of objects also
helps in boosting performance; and (2) our method also extends well to
non-English image captioning, which usually suffers from a scarcer level of
annotations. Our findings are supported by strong empirical results. Our code
is available at https://github.com/zihangm/obj-centric-unsup-caption.Comment: ECCV 202
Multi-Task Video Captioning with Video and Entailment Generation
Video captioning, the task of describing the content of a video, has seen
some promising improvements in recent years with sequence-to-sequence models,
but accurately learning the temporal and logical dynamics involved in the task
still remains a challenge, especially given the lack of sufficient annotated
data. We improve video captioning by sharing knowledge with two related
directed-generation tasks: a temporally-directed unsupervised video prediction
task to learn richer context-aware video encoder representations, and a
logically-directed language entailment generation task to learn better
video-entailed caption decoder representations. For this, we present a
many-to-many multi-task learning model that shares parameters across the
encoders and decoders of the three tasks. We achieve significant improvements
and the new state-of-the-art on several standard video captioning datasets
using diverse automatic and human evaluations. We also show mutual multi-task
improvements on the entailment generation task.Comment: ACL 2017 (14 pages w/ supplementary
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