Titlebook: Domain Adaptation for Visual Understanding; Richa Singh,Mayank Vatsa,Nalini Ratha Book 2020 Springer Nature Switzerland AG 2020 Domain Ada

態(tài)學(xué)

Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating,es, the obtained intermediate response map can fit the learning rate well, which will effectively alleviate the learning-related model degradation. The evaluations on the benchmark datasets KITTI and VOT2017 demonstrate that the proposed tracker outperforms the existing CF-based models, with advantages regarding the tracking accuracy.

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流動(dòng)才波動(dòng)

Richa Singh,Mayank Vatsa,Nalini RathaPresents the latest research on domain adaptation for visual understanding.Provides perspectives from an international selection of authorities in the field.Reviews a variety of applications and techn

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palpitate

轉(zhuǎn)向

Decision-Making Across Culturesfy an unlabeled “target” dataset by leveraging a labeled “source” dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminative domain adaptation?(M-ADDA) which performs two main steps. First, it uses a metric learning approach to train the source model

Excise

Satheesh Gunaga,Jonathan Zygowiecpping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce ., a dual adversarial auto-encoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the d

Ingredient

Shauna Gibbons,Christian T. Sinclairearns the small dataset as a transfer task from a larger source dataset. Transfer Learning?can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate for the layers of the neural network. We show through experiments on

ACE-inhibitor

Alan Elbaum,Lucia Kinsey,Jeffrey Marianoared with video language retrieval, video segment retrieval?is a novel task that uses natural language to retrieve a specific video segment from the whole video. One common method is to learn a similarity metric between video and language features. In this chapter, we utilize ensemble learning?metho