Computer Vision – ECCV 2024 Workshops

TRAMA

The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024.  These LNCS volumes contain 574 accepted papers from 53 of the 73 workshops. The list of workshops and distribution of the workshop papers in the LNCS volumes can be found in the preface that is freely accessible online.      

SOMMARIO

.- Landmark-Based Screening: Femoral Head Coverage and Graf Classificationin Infant Developmental Dysplasia of the Hip..- MVTN: A Multiscale Video Transformer Network for Hand Gesture Recognition..- One-Shot Image Restoration..- Medical Image Segmentation with SAM-generated Annotations..- Manipulating and Mitigating Generative Model Biases without Retraining..- Fake or JPEG? Revealing Common Biases in Generated Image DetectionDatasets..- Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image GenerativeModels..- A semiotic methodology for assessing the compositional effectiveness of generativetext-to-image models (Midjourney and DALLoE)..- A Framework for Critical Evaluation of Text-to-Image Models: IntegratingArt Historical Analysis, Artistic Exploration, and Critical Prompt Engineering..- Civiverse: A Dataset for Analyzing User Engagement with Open-SourceTTI-Models..- Exploring the Boundaries of Content Moderation in Text-to-Image Generation..- Rethinking HTG Evaluation: Bridging Generation and Recognition..- Evaluation Framework for Feedback Generation Methods in Skeletal MovementAssessment..- FaceOracle: Chat with a Face Image Oracle..- Makeup-Guided Facial Privacy Protection via Untrained Neural NetworkPriors..- How to Squeeze An Explanation Out of Your Model..- How were you created? Explaining synthetic face images generated by diffusionmodels..- Frequency Matters: Explaining Biases of Face Recognition in the FrequencyDomain..- How green is continual learning, really? Analyzing the energy consumptionin continual training of vision foundation models..- Architecture-Agnostic Unsupervised Gradient Regularization ForParameter-Efficient Transfer Learning..- Foundation Model or Finetune? Evaluation of few-shot semantic segmentationfor river pollution..- Personalizing Multimodal Large Language Models for Image Captioning: AnExperimental Analysis..- Improved Baselines for Data-efficient Perceptual Augmentation of LLMs..- Watt for What: Rethinking Deep Learning’s Energy-Performance Relationship.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031920882
• Collana: Lecture Notes in Computer Science
• Dimensioni: 235 x 155 mm
• Formato: Brossura
• Illustration Notes: LV, 408 p. 146 illus., 141 illus. in color.
• Pagine Arabe: 408
• Pagine Romane: lv