32,298 research outputs found
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows
both text and sketch as input modalities for the query. A cross-modal deep
network architecture is formulated to jointly model the sketch and text input
modalities as well as the the image output modality, learning a common
embedding between text and images and between sketches and images. In addition,
an attention model is used to selectively focus the attention on the different
objects of the image, allowing for retrieval with multiple objects in the
query. Experiments show that the proposed method performs the best in both
single and multiple object image retrieval in standard datasets.Comment: Accepted at ICPR 201
DeepBox: Learning Objectness with Convolutional Networks
Existing object proposal approaches use primarily bottom-up cues to rank
proposals, while we believe that objectness is in fact a high level construct.
We argue for a data-driven, semantic approach for ranking object proposals. Our
framework, which we call DeepBox, uses convolutional neural networks (CNNs) to
rerank proposals from a bottom-up method. We use a novel four-layer CNN
architecture that is as good as much larger networks on the task of evaluating
objectness while being much faster. We show that DeepBox significantly improves
over the bottom-up ranking, achieving the same recall with 500 proposals as
achieved by bottom-up methods with 2000. This improvement generalizes to
categories the CNN has never seen before and leads to a 4.5-point gain in
detection mAP. Our implementation achieves this performance while running at
260 ms per image.Comment: ICCV 2015 Camera-ready versio
Asymmetric Feature Maps with Application to Sketch Based Retrieval
We propose a novel concept of asymmetric feature maps (AFM), which allows to
evaluate multiple kernels between a query and database entries without
increasing the memory requirements. To demonstrate the advantages of the AFM
method, we derive a short vector image representation that, due to asymmetric
feature maps, supports efficient scale and translation invariant sketch-based
image retrieval. Unlike most of the short-code based retrieval systems, the
proposed method provides the query localization in the retrieved image. The
efficiency of the search is boosted by approximating a 2D translation search
via trigonometric polynomial of scores by 1D projections. The projections are a
special case of AFM. An order of magnitude speed-up is achieved compared to
traditional trigonometric polynomials. The results are boosted by an
image-based average query expansion, exceeding significantly the state of the
art on standard benchmarks.Comment: CVPR 201
Class-Weighted Convolutional Features for Visual Instance Search
Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been proven effective feature extractors for image retrieval. The
most successful approaches are based on encoding the activations of
convolutional layers, as they convey the image spatial information. In this
paper, we go beyond this spatial information and propose a local-aware encoding
of convolutional features based on semantic information predicted in the target
image. To this end, we obtain the most discriminative regions of an image using
Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the
network and therefore, our approach, has the additional advantage of not
requiring external information. In addition, we use CAMs to generate object
proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval,
Oxford5k and Paris6k, demonstrate the competitiveness of our approach
outperforming the current state-of-the-art when using off-the-shelf models
trained on ImageNet. The source code and model used in this paper are publicly
available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September
201
Gaze Embeddings for Zero-Shot Image Classification
Zero-shot image classification using auxiliary information, such as
attributes describing discriminative object properties, requires time-consuming
annotation by domain experts. We instead propose a method that relies on human
gaze as auxiliary information, exploiting that even non-expert users have a
natural ability to judge class membership. We present a data collection
paradigm that involves a discrimination task to increase the information
content obtained from gaze data. Our method extracts discriminative descriptors
from the data and learns a compatibility function between image and gaze using
three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid
(GFG) and Gaze Features with Sequence (GFS). We introduce two new
gaze-annotated datasets for fine-grained image classification and show that
human gaze data is indeed class discriminative, provides a competitive
alternative to expert-annotated attributes, and outperforms other baselines for
zero-shot image classification
- …