24,290 research outputs found
Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition
This paper makes three main contributions to the theory of communication
complexity and stream computation. First, we present new bounds on the
information complexity of AUGMENTED-INDEX. In contrast to analogous results for
INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the
significant technical challenge that protocols for AUGMENTED-INDEX may violate
the "rectangle property" due to the inherent input sharing. Second, we use
these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC,
2010] that asked about the multi-pass complexity of recognizing Dyck languages.
This results in a natural separation between the standard multi-pass model and
the multi-pass model that permits reverse passes. Third, we present the first
passive memory checkers that verify the interaction transcripts of priority
queues, stacks, and double-ended queues. We obtain tight upper and lower bounds
for these problems, thereby addressing an important sub-class of the memory
checking framework of Blum et al. [Algorithmica, 1994]
An item/order tradeoff explanation of word length and generation effects
[Abstract]: The item-order hypothesis suggests that under certain conditions increased item processing can lead to deficits in order processing, and that this produces a dissociation in performance between item and order tasks. The generation effect is one such example. The word length effect is seen as another instance where this tradeoff might be observed. The following experiments compare word length and generation effects under serial recall and single item recognition conditions. Short words are better recalled than long words on the serial recall task but long words were better recognised than short words. The results are consistent with the item-order approach and support a novel explanation for the word length effect
Jointly Modeling Embedding and Translation to Bridge Video and Language
Automatically describing video content with natural language is a fundamental
challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence
dynamics, has attracted increasing attention on visual interpretation. However,
most existing approaches generate a word locally with given previous words and
the visual content, while the relationship between sentence semantics and
visual content is not holistically exploited. As a result, the generated
sentences may be contextually correct but the semantics (e.g., subjects, verbs
or objects) are not true.
This paper presents a novel unified framework, named Long Short-Term Memory
with visual-semantic Embedding (LSTM-E), which can simultaneously explore the
learning of LSTM and visual-semantic embedding. The former aims to locally
maximize the probability of generating the next word given previous words and
visual content, while the latter is to create a visual-semantic embedding space
for enforcing the relationship between the semantics of the entire sentence and
visual content. Our proposed LSTM-E consists of three components: a 2-D and/or
3-D deep convolutional neural networks for learning powerful video
representation, a deep RNN for generating sentences, and a joint embedding
model for exploring the relationships between visual content and sentence
semantics. The experiments on YouTube2Text dataset show that our proposed
LSTM-E achieves to-date the best reported performance in generating natural
sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also
demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO)
triplets to several state-of-the-art techniques
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
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