314,752 research outputs found
Learning Multimodal Latent Attributes
Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning
TransNet: A Transfer Learning-Based Network for Human Action Recognition
Human action recognition (HAR) is a high-level and significant research area
in computer vision due to its ubiquitous applications. The main limitations of
the current HAR models are their complex structures and lengthy training time.
In this paper, we propose a simple yet versatile and effective end-to-end deep
learning architecture, coined as TransNet, for HAR. TransNet decomposes the
complex 3D-CNNs into 2D- and 1D-CNNs, where the 2D- and 1D-CNN components
extract spatial features and temporal patterns in videos, respectively.
Benefiting from its concise architecture, TransNet is ideally compatible with
any pretrained state-of-the-art 2D-CNN models in other fields, being
transferred to serve the HAR task. In other words, it naturally leverages the
power and success of transfer learning for HAR, bringing huge advantages in
terms of efficiency and effectiveness. Extensive experimental results and the
comparison with the state-of-the-art models demonstrate the superior
performance of the proposed TransNet in HAR in terms of flexibility, model
complexity, training speed and classification accuracy
Convolutional Drift Networks for Video Classification
Analyzing spatio-temporal data like video is a challenging task that requires
processing visual and temporal information effectively. Convolutional Neural
Networks have shown promise as baseline fixed feature extractors through
transfer learning, a technique that helps minimize the training cost on visual
information. Temporal information is often handled using hand-crafted features
or Recurrent Neural Networks, but this can be overly specific or prohibitively
complex. Building a fully trainable system that can efficiently analyze
spatio-temporal data without hand-crafted features or complex training is an
open challenge. We present a new neural network architecture to address this
challenge, the Convolutional Drift Network (CDN). Our CDN architecture combines
the visual feature extraction power of deep Convolutional Neural Networks with
the intrinsically efficient temporal processing provided by Reservoir
Computing. In this introductory paper on the CDN, we provide a very simple
baseline implementation tested on two egocentric (first-person) video activity
datasets.We achieve video-level activity classification results on-par with
state-of-the art methods. Notably, performance on this complex spatio-temporal
task was produced by only training a single feed-forward layer in the CDN.Comment: Published in IEEE Rebooting Computin
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Short internet video clips like vines present a significantly wild
distribution compared to traditional video datasets. In this paper, we focus on
the problem of unsupervised action classification in wild vines using
traditional labeled datasets. To this end, we use a data augmentation based
simple domain adaptation strategy. We utilise semantic word2vec space as a
common subspace to embed video features from both, labeled source domain and
unlablled target domain. Our method incrementally augments the labeled source
with target samples and iteratively modifies the embedding function to bring
the source and target distributions together. Additionally, we utilise a
multi-modal representation that incorporates noisy semantic information
available in form of hash-tags. We show the effectiveness of this simple
adaptation technique on a test set of vines and achieve notable improvements in
performance.Comment: 9 pages, GCPR, 201
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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