1,027 research outputs found
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealing with the scarcity of training data. Fortunately, the ultra-fine
granularity naturally brings rich relationships among object classes. This
paper proposes a novel approach to exploit the rich relationships through
bipartite-graph labels (BGL). We show how to model BGL in an overall
convolutional neural networks and the resulting system can be optimized through
back-propagation. We also show that it is computationally efficient in
inference thanks to the bipartite structure. To facilitate the study, we
construct a new food benchmark dataset, which consists of 37,885 food images
collected from 6 restaurants and totally 975 menus. Experimental results on
this new food and three other datasets demonstrates BGL advances previous works
in fine-grained object recognition. An online demo is available at
http://www.f-zhou.com/fg_demo/
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the performance of recognition and robust feature
representation has not been well studied. In this paper, we propose to leverage
intra-class variance in metric learning of triplet network to improve the
performance of fine-grained recognition. Through partitioning training images
within each category into a few groups, we form the triplet samples across
different categories as well as different groups, which is called Group
Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is
strengthened by incorporating intra-class variance with GS-TRS, which may
contribute to the optimization objective of triplet network. Extensive
experiments over benchmark datasets CompCar and VehicleID show that the
proposed GS-TRS has significantly outperformed state-of-the-art approaches in
both classification and retrieval tasks.Comment: 6 pages, 5 figure
Deep Learning for Semantic Part Segmentation with High-Level Guidance
In this work we address the task of segmenting an object into its parts, or
semantic part segmentation. We start by adapting a state-of-the-art semantic
segmentation system to this task, and show that a combination of a
fully-convolutional Deep CNN system coupled with Dense CRF labelling provides
excellent results for a broad range of object categories. Still, this approach
remains agnostic to high-level constraints between object parts. We introduce
such prior information by means of the Restricted Boltzmann Machine, adapted to
our task and train our model in an discriminative fashion, as a hidden CRF,
demonstrating that prior information can yield additional improvements. We also
investigate the performance of our approach ``in the wild'', without
information concerning the objects' bounding boxes, using an object detector to
guide a multi-scale segmentation scheme. We evaluate the performance of our
approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing
and face labelling respectively. We show superior performance with respect to
competitive methods that have been extensively engineered on these benchmarks,
as well as realistic qualitative results on part segmentation, even for
occluded or deformable objects. We also provide quantitative and extensive
qualitative results on three classes from the PASCAL Parts dataset. Finally, we
show that our multi-scale segmentation scheme can boost accuracy, recovering
segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table
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