8,127 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
Learning to Retrieve Videos by Asking Questions
The majority of traditional text-to-video retrieval systems operate in static
environments, i.e., there is no interaction between the user and the agent
beyond the initial textual query provided by the user. This can be sub-optimal
if the initial query has ambiguities, which would lead to many falsely
retrieved videos. To overcome this limitation, we propose a novel framework for
Video Retrieval using Dialog (ViReD), which enables the user to interact with
an AI agent via multiple rounds of dialog, where the user refines retrieved
results by answering questions generated by an AI agent. Our novel multimodal
question generator learns to ask questions that maximize the subsequent video
retrieval performance using (i) the video candidates retrieved during the last
round of interaction with the user and (ii) the text-based dialog history
documenting all previous interactions, to generate questions that incorporate
both visual and linguistic cues relevant to video retrieval. Furthermore, to
generate maximally informative questions, we propose an Information-Guided
Supervision (IGS), which guides the question generator to ask questions that
would boost subsequent video retrieval accuracy. We validate the effectiveness
of our interactive ViReD framework on the AVSD dataset, showing that our
interactive method performs significantly better than traditional
non-interactive video retrieval systems. We also demonstrate that our proposed
approach generalizes to the real-world settings that involve interactions with
real humans, thus, demonstrating the robustness and generality of our framewor
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
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