4,930 research outputs found
Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images
We address the problem of fine-grained action localization from temporally
untrimmed web videos. We assume that only weak video-level annotations are
available for training. The goal is to use these weak labels to identify
temporal segments corresponding to the actions, and learn models that
generalize to unconstrained web videos. We find that web images queried by
action names serve as well-localized highlights for many actions, but are
noisily labeled. To solve this problem, we propose a simple yet effective
method that takes weak video labels and noisy image labels as input, and
generates localized action frames as output. This is achieved by cross-domain
transfer between video frames and web images, using pre-trained deep
convolutional neural networks. We then use the localized action frames to train
action recognition models with long short-term memory networks. We collect a
fine-grained sports action data set FGA-240 of more than 130,000 YouTube
videos. It has 240 fine-grained actions under 85 sports activities. Convincing
results are shown on the FGA-240 data set, as well as the THUMOS 2014
localization data set with untrimmed training videos.Comment: Camera ready version for ACM Multimedia 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
Compositional Model based Fisher Vector Coding for Image Classification
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and
Machine Intelligence (TPAMI
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