29 research outputs found
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state of a
discrete Markov random field (MRF), also known as the MRF energy minimization
problem. The task is known to be NP-hard in general and its practical
importance motivates numerous approximate algorithms. We propose a submodular
relaxation approach (SMR) based on a Lagrangian relaxation of the initial
problem. Unlike the dual decomposition approach of Komodakis et al., 2011 SMR
does not decompose the graph structure of the initial problem but constructs a
submodular energy that is minimized within the Lagrangian relaxation. Our
approach is applicable to both pairwise and high-order MRFs and allows to take
into account global potentials of certain types. We study theoretical
properties of the proposed approach and evaluate it experimentally.Comment: This paper is accepted for publication in IEEE Transactions on
Pattern Analysis and Machine Intelligenc
Context-aware CNNs for person head detection
Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.Comment: To appear in International Conference on Computer Vision (ICCV), 201
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning
high-dimensional word representations that capture rich semantic relationships
between words. However, Skip-gram as well as most prior work on learning word
representations does not take into account word ambiguity and maintain only
single representation per word. Although a number of Skip-gram modifications
were proposed to overcome this limitation and learn multi-prototype word
representations, they either require a known number of word meanings or learn
them using greedy heuristic approaches. In this paper we propose the Adaptive
Skip-gram model which is a nonparametric Bayesian extension of Skip-gram
capable to automatically learn the required number of representations for all
words at desired semantic resolution. We derive efficient online variational
learning algorithm for the model and empirically demonstrate its efficiency on
word-sense induction task
Tensorizing Neural Networks
Deep neural networks currently demonstrate state-of-the-art performance in
several domains. At the same time, models of this class are very demanding in
terms of computational resources. In particular, a large amount of memory is
required by commonly used fully-connected layers, making it hard to use the
models on low-end devices and stopping the further increase of the model size.
In this paper we convert the dense weight matrices of the fully-connected
layers to the Tensor Train format such that the number of parameters is reduced
by a huge factor and at the same time the expressive power of the layer is
preserved. In particular, for the Very Deep VGG networks we report the
compression factor of the dense weight matrix of a fully-connected layer up to
200000 times leading to the compression factor of the whole network up to 7
times
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Object detection in video is crucial for many applications. Compared to
images, video provides additional cues which can help to disambiguate the
detection problem. Our goal in this paper is to learn discriminative models for
the temporal evolution of object appearance and to use such models for object
detection. To model temporal evolution, we introduce space-time tubes
corresponding to temporal sequences of bounding boxes. We propose two CNN
architectures for generating and classifying tubes, respectively. Our tube
proposal network (TPN) first generates a large number of spatio-temporal tube
proposals maximizing object recall. The Tube-CNN then implements a tube-level
object detector in the video. Our method improves state of the art on two
large-scale datasets for object detection in video: HollywoodHeads and ImageNet
VID. Tube models show particular advantages in difficult dynamic scenes.Comment: 13 pages, 8 figures, technical repor
Modeling Spatio-Temporal Human Track Structure for Action Localization
This paper addresses spatio-temporal localization of human actions in video.
In order to localize actions in time, we propose a recurrent localization
network (RecLNet) designed to model the temporal structure of actions on the
level of person tracks. Our model is trained to simultaneously recognize and
localize action classes in time and is based on two layer gated recurrent units
(GRU) applied separately to two streams, i.e. appearance and optical flow
streams. When used together with state-of-the-art person detection and
tracking, our model is shown to improve substantially spatio-temporal action
localization in videos. The gain is shown to be mainly due to improved temporal
localization. We evaluate our method on two recent datasets for spatio-temporal
action localization, UCF101-24 and DALY, demonstrating a significant
improvement of the state of the art