76,009 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/
ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity
We present ALADIN (All Layer AdaIN); a novel architecture for searching
images based on the similarity of their artistic style. Representation learning
is critical to visual search, where distance in the learned search embedding
reflects image similarity. Learning an embedding that discriminates
fine-grained variations in style is hard, due to the difficulty of defining and
labelling style. ALADIN takes a weakly supervised approach to learning a
representation for fine-grained style similarity of digital artworks,
leveraging BAM-FG, a novel large-scale dataset of user generated content
groupings gathered from the web. ALADIN sets a new state of the art accuracy
for style-based visual search over both coarse labelled style data (BAM) and
BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style
groupings also contributed by this work
Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search
Text-based person search (TBPS) is a challenging task that aims to search
pedestrian images with the same identity from an image gallery given a query
text. In recent years, TBPS has made remarkable progress and state-of-the-art
methods achieve superior performance by learning local fine-grained
correspondence between images and texts. However, most existing methods rely on
explicitly generated local parts to model fine-grained correspondence between
modalities, which is unreliable due to the lack of contextual information or
the potential introduction of noise. Moreover, existing methods seldom consider
the information inequality problem between modalities caused by image-specific
information. To address these limitations, we propose an efficient joint
Multi-level Alignment Network (MANet) for TBPS, which can learn aligned
image/text feature representations between modalities at multiple levels, and
realize fast and effective person search. Specifically, we first design an
image-specific information suppression module, which suppresses image
background and environmental factors by relation-guided localization and
channel attention filtration respectively. This module effectively alleviates
the information inequality problem and realizes the alignment of information
volume between images and texts. Secondly, we propose an implicit local
alignment module to adaptively aggregate all pixel/word features of image/text
to a set of modality-shared semantic topic centers and implicitly learn the
local fine-grained correspondence between modalities without additional
supervision and cross-modal interactions. And a global alignment is introduced
as a supplement to the local perspective. The cooperation of global and local
alignment modules enables better semantic alignment between modalities.
Extensive experiments on multiple databases demonstrate the effectiveness and
superiority of our MANet
An explorative study of interface support for image searching
In this paper we study interfaces for image retrieval systems. Current image retrieval interfaces are limited to providing query facilities and result presentation. The user can inspect the results and possibly provide feedback on their relevance for the current query. Our approach, in contrast, encourages the user to group and organise their search results and thus provide more fine-grained feedback for the system. It combines the search and management process, which - according to our hypothesis - helps the user to onceptualise their search tasks and to overcome the query formulation problem. An evaluation, involving young design-professionals and di®erent types of information seeking scenarios, shows that the proposed approach succeeds in encouraging the user to conceptualise their tasks and that it leads to increased user satisfaction. However, it could not be shown to increase performance. We identify the problems in the current setup, which when eliminated should lead to more effective searching overall
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