Muutke küpsiste eelistusi

E-raamat: Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part VI

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 80,26 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part VISuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics.

TaylorShift: Shifting the Complexity of Self-Attention from Squared to
Linear (and Back) using Taylor-Softmax.- Balancing Accuracy and Efficiency in
Budget-Aware Early-Exiting Neural Networks.- An Evolutionary Search-Based
Operator Fusion Method with Binary Representation for Deep Learning Inference
Acceleration.- SemFaceEdit: Semantic Face Editing on Generative Radiance
Manifolds.- (D^2)Styler: Advancing Arbitrary Style Transfer with Discrete
Diffusion Methods.- Mask-ControlNet: Higher-Quality Image Generation with An
Additional Mask Prompt.- Freestyle 3D-Aware Portrait Synthesis Based on
Compositional Generative Priors.- FUGAN: A GAN Based Facial Reconstructor For
Accurate Unveiling Of Hidden Faces.- Text2Street: Controllable Text-to-image
Generation for Street Views.- Make An Image Move: Few-shot based Video
Generation Guided by CLIP.- A Framework For Image Synthesis Using Supervised
Contrastive Learning.- TMCSPEECH: A CHINESE TV AND MOVIE SPEECH DATASET WITH
CHARACTER DESCRIPTIONS AND A CHARACTER-BASED VOICE GENERATION MODEL.-
Deterministic Synthesis of Defect Images using Null Optimization.- Adaptive
Refiner based Few-Shot Font Generation.- Controllable 3D object Generation
with Single Image Prompt.- Beyond Labels: Aligning Large Language Models with
Human-like Reasoning.- HindiLLM: Large Language Model for Hindi.- StableTalk:
Advancing Audio-to-Talking Face Generation with Stable Diffusion And Vision
Transformer.- Can LLMs perform structured graph reasoning tasks?.- Improved
Zero-Shot Image Editing via Null-Toon and Directed Delta Denoising Score.-
Texture  Spectral Decorrelation  Criteria.- A Low Rank Gaussian Mixture
Latent Model for Face Generation.- Domain Adaptation for Machinery Fault
Diagnosis Based on Critic Classifier GAN.- Data Augmentation Pipeline for
Enhanced UAV Surveillance.- Generative Adversarial Networks for Imputing
Sparse Learning Performance.- SWave: Improving Vocoder Efficiency by
Straightening the Waveform Generation Path.- Outdoor Scene Relighting with
Diffusion Models.- Matching aggregate posteriors in the variational
autoencoder.- Efficient Nonlinear DAG Learning under Projection Framework.-
GCompletor: A Graph-based Deep Learning Method for Traffic State Imputation
on Urban Road Networks.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97830317817286e.html