000			04679nam a22005535i 4500
001			978-3-030-30671-7
003			DE-He213
005			20240423125120.0
007			cr nn 008mamaa
008			200108s2020 sz | s |||| 0|eng d
020			_a9783030306717 _9978-3-030-30671-7
024	7		_a10.1007/978-3-030-30671-7 _2doi
050		4	_aTA1634
072		7	_aUYQV _2bicssc
072		7	_aCOM012000 _2bisacsh
072		7	_aUYQV _2thema
082	0	4	_a006.37 _223
245	1	0	_aDomain Adaptation for Visual Understanding _h[electronic resource] / _cedited by Richa Singh, Mayank Vatsa, Vishal M. Patel, Nalini Ratha.
250			_a1st ed. 2020.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020.
300			_aX, 144 p. 62 illus., 56 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aDomain Adaptation for Visual Understanding -- M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning -- XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings -- Improving Transferability of Deep Neural Networks -- Cross Modality Video Segment Retrieval with Ensemble Learning -- On Minimum Discrepancy Estimation for Deep Domain Adaptation -- Multi-Modal Conditional Feature Enhancement for Facial Action Unit Recognition -- Intuition Learning -- Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating.
520			_aThis unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: Reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach Introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning Proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks Describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance Presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation Examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding. Dr. Richa Singh is a Professor at Indraprastha Institute of Information Technology, Delhi, India. Dr. Mayank Vatsa is a Professor at the same institution. Dr. Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. Dr. Nalini Ratha is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
650		0	_aComputer vision.
650		0	_aArtificial intelligence.
650		0	_aComputational intelligence.
650	1	4	_aComputer Vision.
650	2	4	_aArtificial Intelligence.
650	2	4	_aComputational Intelligence.
700	1		_aSingh, Richa. _eeditor. _0(orcid)0000-0003-4060-4573 _1https://orcid.org/0000-0003-4060-4573 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aVatsa, Mayank. _eeditor. _0(orcid)0000-0001-5952-2274 _1https://orcid.org/0000-0001-5952-2274 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPatel, Vishal M. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aRatha, Nalini. _eeditor. _0(orcid)0000-0001-7913-5722 _1https://orcid.org/0000-0001-7913-5722 _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030306700
776	0	8	_iPrinted edition: _z9783030306724
776	0	8	_iPrinted edition: _z9783030306731
856	4	0	_uhttps://doi.org/10.1007/978-3-030-30671-7
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c174410 _d174410