1,366 research outputs found
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
We propose a weakly-supervised approach that takes image-sentence pairs as
input and learns to visually ground (i.e., localize) arbitrary linguistic
phrases, in the form of spatial attention masks. Specifically, the model is
trained with images and their associated image-level captions, without any
explicit region-to-phrase correspondence annotations. To this end, we introduce
an end-to-end model which learns visual groundings of phrases with two types of
carefully designed loss functions. In addition to the standard discriminative
loss, which enforces that attended image regions and phrases are consistently
encoded, we propose a novel structural loss which makes use of the parse tree
structures induced by the sentences. In particular, we ensure complementarity
among the attention masks that correspond to sibling noun phrases, and
compositionality of attention masks among the children and parent phrases, as
defined by the sentence parse tree. We validate the effectiveness of our
approach on the Microsoft COCO and Visual Genome datasets.Comment: CVPR 201
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Deep models are the defacto standard in visual decision problems due to their
impressive performance on a wide array of visual tasks. On the other hand,
their opaqueness has led to a surge of interest in explainable systems. In this
work, we emphasize the importance of model explanation in various forms such as
visual pointing and textual justification. The lack of data with justification
annotations is one of the bottlenecks of generating multimodal explanations.
Thus, we propose two large-scale datasets with annotations that visually and
textually justify a classification decision for various activities, i.e. ACT-X,
and for question answering, i.e. VQA-X. We also introduce a multimodal
methodology for generating visual and textual explanations simultaneously. We
quantitatively show that training with the textual explanations not only yields
better textual justification models, but also models that better localize the
evidence that support their decision.Comment: arXiv admin note: text overlap with arXiv:1612.0475
Neural Baby Talk
We introduce a novel framework for image captioning that can produce natural
language explicitly grounded in entities that object detectors find in the
image. Our approach reconciles classical slot filling approaches (that are
generally better grounded in images) with modern neural captioning approaches
(that are generally more natural sounding and accurate). Our approach first
generates a sentence `template' with slot locations explicitly tied to specific
image regions. These slots are then filled in by visual concepts identified in
the regions by object detectors. The entire architecture (sentence template
generation and slot filling with object detectors) is end-to-end
differentiable. We verify the effectiveness of our proposed model on different
image captioning tasks. On standard image captioning and novel object
captioning, our model reaches state-of-the-art on both COCO and Flickr30k
datasets. We also demonstrate that our model has unique advantages when the
train and test distributions of scene compositions -- and hence language priors
of associated captions -- are different. Code has been made available at:
https://github.com/jiasenlu/NeuralBabyTalkComment: 12 pages, 7 figures, CVPR 201
Visual7W: Grounded Question Answering in Images
We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's capacity for deep image understanding. Previous works have
established a loose, global association between QA sentences and images.
However, many questions and answers, in practice, relate to local regions in
the images. We establish a semantic link between textual descriptions and image
regions by object-level grounding. It enables a new type of QA with visual
answers, in addition to textual answers used in previous work. We study the
visual QA tasks in a grounded setting with a large collection of 7W
multiple-choice QA pairs. Furthermore, we evaluate human performance and
several baseline models on the QA tasks. Finally, we propose a novel LSTM model
with spatial attention to tackle the 7W QA tasks.Comment: CVPR 201
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