577 research outputs found
Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs
This paper introduces a novel algorithm for transductive inference in
higher-order MRFs, where the unary energies are parameterized by a variable
classifier. The considered task is posed as a joint optimization problem in the
continuous classifier parameters and the discrete label variables. In contrast
to prior approaches such as convex relaxations, we propose an advantageous
decoupling of the objective function into discrete and continuous subproblems
and a novel, efficient optimization method related to ADMM. This approach
preserves integrality of the discrete label variables and guarantees global
convergence to a critical point. We demonstrate the advantages of our approach
in several experiments including video object segmentation on the DAVIS data
set and interactive image segmentation
Semantic Embedding Space for Zero-Shot Action Recognition
The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is constructed between visual features and a human interpretable
semantic description of each category, allowing categories to be recognised in
the absence of any training data. Existing ZSL studies focus primarily on image
data, and attribute-based semantic representations. In this paper, we address
zero-shot recognition in contemporary video action recognition tasks, using
semantic word vector space as the common space to embed videos and category
labels. This is more challenging because the mapping between the semantic space
and space-time features of videos containing complex actions is more complex
and harder to learn. We demonstrate that a simple self-training and data
augmentation strategy can significantly improve the efficacy of this mapping.
Experiments on human action datasets including HMDB51 and UCF101 demonstrate
that our approach achieves the state-of-the-art zero-shot action recognition
performance.Comment: 5 page
Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation
Abstract. Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the seman-tic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inher-ent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift prob-lem and propose a novel framework, transductive multi-view embedding, to solve it. It is ‘transductive ’ in that unlabelled target data points are explored for projection adaptation, and ‘multi-view ’ in that both low-level feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through ex-tensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complemen-tarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) en-ables novel cross-view annotation tasks.
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
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