1,988 research outputs found
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201
One-Shot Fine-Grained Instance Retrieval
Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale fine-grained identification without a complete training
set. Aiming to conquer this issue, we propose a retrieval task named One-Shot
Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of
identifying unseen objects through a fine-grained retrieval task assisted with
an incomplete auxiliary training set. This paper first presents the detailed
description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we
propose the Convolutional and Normalization Networks (CN-Nets) learned on the
auxiliary dataset to generate a concise and discriminative representation.
Finally, we present a coarse-to-fine retrieval framework consisting of three
components, i.e., coarse retrieval, fine-grained retrieval, and query
expansion, respectively. The framework progressively retrieves images with
similar semantics, and performs fine-grained identification. Experiments show
our OSFGIR framework achieves significantly better accuracy and efficiency than
existing FGVC and image retrieval methods, thus could be a better solution for
large-scale fine-grained object identification.Comment: Accepted by MM2017, 9 pages, 7 figure
Iterative Object and Part Transfer for Fine-Grained Recognition
The aim of fine-grained recognition is to identify sub-ordinate categories in
images like different species of birds. Existing works have confirmed that, in
order to capture the subtle differences across the categories, automatic
localization of objects and parts is critical. Most approaches for object and
part localization relied on the bottom-up pipeline, where thousands of region
proposals are generated and then filtered by pre-trained object/part models.
This is computationally expensive and not scalable once the number of
objects/parts becomes large. In this paper, we propose a nonparametric
data-driven method for object and part localization. Given an unlabeled test
image, our approach transfers annotations from a few similar images retrieved
in the training set. In particular, we propose an iterative transfer strategy
that gradually refine the predicted bounding boxes. Based on the located
objects and parts, deep convolutional features are extracted for recognition.
We evaluate our approach on the widely-used CUB200-2011 dataset and a new and
large dataset called Birdsnap. On both datasets, we achieve better results than
many state-of-the-art approaches, including a few using oracle (manually
annotated) bounding boxes in the test images.Comment: To appear in ICME 2017 as an oral pape
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