29,790 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation
Image annotation aims to annotate a given image with a variable number of
class labels corresponding to diverse visual concepts. In this paper, we
address two main issues in large-scale image annotation: 1) how to learn a rich
feature representation suitable for predicting a diverse set of visual concepts
ranging from object, scene to abstract concept; 2) how to annotate an image
with the optimal number of class labels. To address the first issue, we propose
a novel multi-scale deep model for extracting rich and discriminative features
capable of representing a wide range of visual concepts. Specifically, a novel
two-branch deep neural network architecture is proposed which comprises a very
deep main network branch and a companion feature fusion network branch designed
for fusing the multi-scale features computed from the main branch. The deep
model is also made multi-modal by taking noisy user-provided tags as model
input to complement the image input. For tackling the second issue, we
introduce a label quantity prediction auxiliary task to the main label
prediction task to explicitly estimate the optimal label number for a given
image. Extensive experiments are carried out on two large-scale image
annotation benchmark datasets and the results show that our method
significantly outperforms the state-of-the-art.Comment: Submited to IEEE TI
Clue: Cross-modal Coherence Modeling for Caption Generation
We use coherence relations inspired by computational models of discourse to
study the information needs and goals of image captioning. Using an annotation
protocol specifically devised for capturing image--caption coherence relations,
we annotate 10,000 instances from publicly-available image--caption pairs. We
introduce a new task for learning inferences in imagery and text, coherence
relation prediction, and show that these coherence annotations can be exploited
to learn relation classifiers as an intermediary step, and also train
coherence-aware, controllable image captioning models. The results show a
dramatic improvement in the consistency and quality of the generated captions
with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
Learning Multimodal Latent Attributes
AbstractâThe rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on which the performance scores are based, predominantly
contain fully visible objects. Therefore, `harder' test images, mimicking the
challenging conditions (e.g. occlusion) in which humans routinely recognize
objects, need to be utilized for benchmarking. To address the concerns
mentioned above, we make two contributions. First, we systematically vary the
level of local object-part content, global detail and spatial context in images
from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies
classifiers' robustness to a range of visibility and contextual settings. The
benchmarking procedure relies on a semantic similarity measure that naturally
addresses potential semantic granularity differences between the category
labels in training and test datasets, thus eliminating manual mapping. We use
our procedure on the PPSS-12 dataset to benchmark top-performing classifiers
trained on the ILSVRC-2012 dataset. Our results show that the proposed
benchmarking procedure enables additional differentiation among
state-of-the-art object classifiers in terms of their ability to handle missing
content and insufficient object detail. Given this capability for additional
differentiation, our approach can potentially supplement existing benchmarking
procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie
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