34 research outputs found
Semantic Image Retrieval via Active Grounding of Visual Situations
We describe a novel architecture for semantic image retrieval---in
particular, retrieval of instances of visual situations. Visual situations are
concepts such as "a boxing match," "walking the dog," "a crowd waiting for a
bus," or "a game of ping-pong," whose instantiations in images are linked more
by their common spatial and semantic structure than by low-level visual
similarity. Given a query situation description, our architecture---called
Situate---learns models capturing the visual features of expected objects as
well the expected spatial configuration of relationships among objects. Given a
new image, Situate uses these models in an attempt to ground (i.e., to create a
bounding box locating) each expected component of the situation in the image
via an active search procedure. Situate uses the resulting grounding to compute
a score indicating the degree to which the new image is judged to contain an
instance of the situation. Such scores can be used to rank images in a
collection as part of a retrieval system. In the preliminary study described
here, we demonstrate the promise of this system by comparing Situate's
performance with that of two baseline methods, as well as with a related
semantic image-retrieval system based on "scene graphs.
Im2Flow: Motion Hallucination from Static Images for Action Recognition
Existing methods to recognize actions in static images take the images at
their face value, learning the appearances---objects, scenes, and body
poses---that distinguish each action class. However, such models are deprived
of the rich dynamic structure and motions that also define human activity. We
propose an approach that hallucinates the unobserved future motion implied by a
single snapshot to help static-image action recognition. The key idea is to
learn a prior over short-term dynamics from thousands of unlabeled videos,
infer the anticipated optical flow on novel static images, and then train
discriminative models that exploit both streams of information. Our main
contributions are twofold. First, we devise an encoder-decoder convolutional
neural network and a novel optical flow encoding that can translate a static
image into an accurate flow map. Second, we show the power of hallucinated flow
for recognition, successfully transferring the learned motion into a standard
two-stream network for activity recognition. On seven datasets, we demonstrate
the power of the approach. It not only achieves state-of-the-art accuracy for
dense optical flow prediction, but also consistently enhances recognition of
actions and dynamic scenes.Comment: Published in CVPR 2018, project page:
http://vision.cs.utexas.edu/projects/im2flow
Neural Motifs: Scene Graph Parsing with Global Context
We investigate the problem of producing structured graph representations of
visual scenes. Our work analyzes the role of motifs: regularly appearing
substructures in scene graphs. We present new quantitative insights on such
repeated structures in the Visual Genome dataset. Our analysis shows that
object labels are highly predictive of relation labels but not vice-versa. We
also find that there are recurring patterns even in larger subgraphs: more than
50% of graphs contain motifs involving at least two relations. Our analysis
motivates a new baseline: given object detections, predict the most frequent
relation between object pairs with the given labels, as seen in the training
set. This baseline improves on the previous state-of-the-art by an average of
3.6% relative improvement across evaluation settings. We then introduce Stacked
Motif Networks, a new architecture designed to capture higher order motifs in
scene graphs that further improves over our strong baseline by an average 7.1%
relative gain. Our code is available at github.com/rowanz/neural-motifs.Comment: CVPR 2018 camera read
Active Object Localization in Visual Situations
We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.Comment: 14 page
Ensembles of Deep Neural Networks for Action Recognition in Still Images
Despite the fact that notable improvements have been made recently in the
field of feature extraction and classification, human action recognition is
still challenging, especially in images, in which, unlike videos, there is no
motion. Thus, the methods proposed for recognizing human actions in videos
cannot be applied to still images. A big challenge in action recognition in
still images is the lack of large enough datasets, which is problematic for
training deep Convolutional Neural Networks (CNNs) due to the overfitting
issue. In this paper, by taking advantage of pre-trained CNNs, we employ the
transfer learning technique to tackle the lack of massive labeled action
recognition datasets. Furthermore, since the last layer of the CNN has
class-specific information, we apply an attention mechanism on the output
feature maps of the CNN to extract more discriminative and powerful features
for classification of human actions. Moreover, we use eight different
pre-trained CNNs in our framework and investigate their performance on Stanford
40 dataset. Finally, we propose using the Ensemble Learning technique to
enhance the overall accuracy of action classification by combining the
predictions of multiple models. The best setting of our method is able to
achieve 93.17 accuracy on the Stanford 40 dataset.Comment: 5 pages, 2 figures, 3 tables, Accepted by ICCKE 201
Expanded Parts Model for Semantic Description of Humans in Still Images
We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.Comment: Accepted for publication in IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI