440,633 research outputs found
Dynamic concept composition for zero-example event detection
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake"). Towards this goal, we first pre-Train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w.r.t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with freeform text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach
DisCLIP: Open-Vocabulary Referring Expression Generation
Referring Expressions Generation (REG) aims to produce textual descriptions
that unambiguously identifies specific objects within a visual scene.
Traditionally, this has been achieved through supervised learning methods,
which perform well on specific data distributions but often struggle to
generalize to new images and concepts. To address this issue, we present a
novel approach for REG, named DisCLIP, short for discriminative CLIP. We build
on CLIP, a large-scale visual-semantic model, to guide an LLM to generate a
contextual description of a target concept in an image while avoiding other
distracting concepts. Notably, this optimization happens at inference time and
does not require additional training or tuning of learned parameters. We
measure the quality of the generated text by evaluating the capability of a
receiver model to accurately identify the described object within the scene. To
achieve this, we use a frozen zero-shot comprehension module as a critique of
our generated referring expressions. We evaluate DisCLIP on multiple referring
expression benchmarks through human evaluation and show that it significantly
outperforms previous methods on out-of-domain datasets. Our results highlight
the potential of using pre-trained visual-semantic models for generating
high-quality contextual descriptions
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A survey of induction algorithms for machine learning
Central to all systems for machine learning from examples is an induction algorithm. The purpose of the algorithm is to generalize from a finite set of training examples a description consistent with the examples seen, and, hopefully, with the potentially infinite set of examples not seen. This paper surveys four machine learning induction algorithms. The knowledge representation schemes and a PDL description of algorithm control are emphasized. System characteristics that are peculiar to a domain of application are de-emphasized. Finally, a comparative summary of the learning algorithms is presented
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Incremental learning of independent, overlapping, and graded concept descriptions with an instance-based process framework
Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to be (1) defined with respect to the same set of relevant attributes, (2) disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We believe that supervised learning algorithms should learn attribute relevancies independently for each concept, allow instances to be members of any subset of concepts, and represent graded concept descriptions. This paper introduces a process framework for instance-based learning algorithms that exploit only specific instance and performance feedback information to guide their concept learning processes. We also introduce Bloom, a specific instantiation of this framework. Bloom is a supervised, incremental, instance-based learning algorithm that learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept memberships. We describe empirical evidence to support our claims that Bloom can learn independent, overlapping, and graded concept descriptions
Learning with Latent Language
The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to show that using the space of natural language strings as a
parameter space is an effective way to capture natural task structure. In a
pretraining phase, we learn a language interpretation model that transforms
inputs (e.g. images) into outputs (e.g. labels) given natural language
descriptions. To learn a new concept (e.g. a classifier), we search directly in
the space of descriptions to minimize the interpreter's loss on training
examples. Crucially, our models do not require language data to learn these
concepts: language is used only in pretraining to impose structure on
subsequent learning. Results on image classification, text editing, and
reinforcement learning show that, in all settings, models with a linguistic
parameterization outperform those without
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
Towards Zero-Shot Frame Semantic Parsing for Domain Scaling
State-of-the-art slot filling models for goal-oriented human/machine
conversational language understanding systems rely on deep learning methods.
While multi-task training of such models alleviates the need for large
in-domain annotated datasets, bootstrapping a semantic parsing model for a new
domain using only the semantic frame, such as the back-end API or knowledge
graph schema, is still one of the holy grail tasks of language understanding
for dialogue systems. This paper proposes a deep learning based approach that
can utilize only the slot description in context without the need for any
labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The
main idea of this paper is to leverage the encoding of the slot names and
descriptions within a multi-task deep learned slot filling model, to implicitly
align slots across domains. The proposed approach is promising for solving the
domain scaling problem and eliminating the need for any manually annotated data
or explicit schema alignment. Furthermore, our experiments on multiple domains
show that this approach results in significantly better slot-filling
performance when compared to using only in-domain data, especially in the low
data regime.Comment: 4 pages + 1 reference
Models of incremental concept formation
Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions
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