784,020 research outputs found
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Recurrent feedback connections in the mammalian visual system have been
hypothesized to play a role in synthesizing input in the theoretical framework
of analysis by synthesis. The comparison of internally synthesized
representation with that of the input provides a validation mechanism during
perceptual inference and learning. Inspired by these ideas, we proposed that
the synthesis machinery can compose new, unobserved images by imagination to
train the network itself so as to increase the robustness of the system in
novel scenarios. As a proof of concept, we investigated whether images composed
by imagination could help an object recognition system to deal with occlusion,
which is challenging for the current state-of-the-art deep convolutional neural
networks. We fine-tuned a network on images containing objects in various
occlusion scenarios, that are imagined or self-generated through a deep
generator network. Trained on imagined occluded scenarios under the object
persistence constraint, our network discovered more subtle and localized image
features that were neglected by the original network for object classification,
obtaining better separability of different object classes in the feature space.
This leads to significant improvement of object recognition under occlusion for
our network relative to the original network trained only on un-occluded
images. In addition to providing practical benefits in object recognition under
occlusion, this work demonstrates the use of self-generated composition of
visual scenes through the synthesis loop, combined with the object persistence
constraint, can provide opportunities for neural networks to discover new
relevant patterns in the data, and become more flexible in dealing with novel
situations.Comment: Accepted by 14th Conference on Computer and Robot Visio
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/
A single-system model predicts recognition memory and repetition priming in amnesia
We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit
A deep learning pipeline for product recognition on store shelves
Recognition of grocery products in store shelves poses peculiar challenges.
Firstly, the task mandates the recognition of an extremely high number of
different items, in the order of several thousands for medium-small shops, with
many of them featuring small inter and intra class variability. Then, available
product databases usually include just one or a few studio-quality images per
product (referred to herein as reference images), whilst at test time
recognition is performed on pictures displaying a portion of a shelf containing
several products and taken in the store by cheap cameras (referred to as query
images). Moreover, as the items on sale in a store as well as their appearance
change frequently over time, a practical recognition system should handle
seamlessly new products/packages. Inspired by recent advances in object
detection and image retrieval, we propose to leverage on state of the art
object detectors based on deep learning to obtain an initial productagnostic
item detection. Then, we pursue product recognition through a similarity search
between global descriptors computed on reference and cropped query images. To
maximize performance, we learn an ad-hoc global descriptor by a CNN trained on
reference images based on an image embedding loss. Our system is
computationally expensive at training time but can perform recognition rapidly
and accurately at test time
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