3,991 research outputs found
Mining Object Parts from CNNs via Active Question-Answering
Given a convolutional neural network (CNN) that is pre-trained for object
classification, this paper proposes to use active question-answering to
semanticize neural patterns in conv-layers of the CNN and mine part concepts.
For each part concept, we mine neural patterns in the pre-trained CNN, which
are related to the target part, and use these patterns to construct an And-Or
graph (AOG) to represent a four-layer semantic hierarchy of the part. As an
interpretable model, the AOG associates different CNN units with different
explicit object parts. We use an active human-computer communication to
incrementally grow such an AOG on the pre-trained CNN as follows. We allow the
computer to actively identify objects, whose neural patterns cannot be
explained by the current AOG. Then, the computer asks human about the
unexplained objects, and uses the answers to automatically discover certain CNN
patterns corresponding to the missing knowledge. We incrementally grow the AOG
to encode new knowledge discovered during the active-learning process. In
experiments, our method exhibits high learning efficiency. Our method uses
about 1/6-1/3 of the part annotations for training, but achieves similar or
better part-localization performance than fast-RCNN methods.Comment: Published in CVPR 201
Comparator Networks
The objective of this work is set-based verification, e.g. to decide if two
sets of images of a face are of the same person or not. The traditional
approach to this problem is to learn to generate a feature vector per image,
aggregate them into one vector to represent the set, and then compute the
cosine similarity between sets. Instead, we design a neural network
architecture that can directly learn set-wise verification. Our contributions
are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of
sets (each may contain a variable number of images) as inputs, and compute a
similarity between the pair--this involves attending to multiple discriminative
local regions (landmarks), and comparing local descriptors between pairs of
faces; (ii) To encourage high-quality representations for each set, internal
competition is introduced for recalibration based on the landmark score; (iii)
Inspired by image retrieval, a novel hard sample mining regime is proposed to
control the sampling process, such that the DCN is complementary to the
standard image classification models. Evaluations on the IARPA Janus face
recognition benchmarks show that the comparator networks outperform the
previous state-of-the-art results by a large margin.Comment: To appear in ECCV 201
Fine-graind Image Classification via Combining Vision and Language
Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obtain the semantic parts for better classification
accuracy. Despite achieving promising results, these methods mainly have two
limitations: (1) not all the parts which obtained through the part detection
models are beneficial and indispensable for classification, and (2)
fine-grained image classification requires more detailed visual descriptions
which could not be provided by the part locations or attribute annotations. For
addressing the above two limitations, this paper proposes the two-stream model
combining vision and language (CVL) for learning latent semantic
representations. The vision stream learns deep representations from the
original visual information via deep convolutional neural network. The language
stream utilizes the natural language descriptions which could point out the
discriminative parts or characteristics for each image, and provides a flexible
and compact way of encoding the salient visual aspects for distinguishing
sub-categories. Since the two streams are complementary, combining the two
streams can further achieves better classification accuracy. Comparing with 12
state-of-the-art methods on the widely used CUB-200-2011 dataset for
fine-grained image classification, the experimental results demonstrate our CVL
approach achieves the best performance.Comment: 9 pages, to appear in CVPR 201
A fine-grained approach to scene text script identification
This paper focuses on the problem of script identification in unconstrained
scenarios. Script identification is an important prerequisite to recognition,
and an indispensable condition for automatic text understanding systems
designed for multi-language environments. Although widely studied for document
images and handwritten documents, it remains an almost unexplored territory for
scene text images.
We detail a novel method for script identification in natural images that
combines convolutional features and the Naive-Bayes Nearest Neighbor
classifier. The proposed framework efficiently exploits the discriminative
power of small stroke-parts, in a fine-grained classification framework.
In addition, we propose a new public benchmark dataset for the evaluation of
joint text detection and script identification in natural scenes. Experiments
done in this new dataset demonstrate that the proposed method yields state of
the art results, while it generalizes well to different datasets and variable
number of scripts. The evidence provided shows that multi-lingual scene text
recognition in the wild is a viable proposition. Source code of the proposed
method is made available online
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