| Record Type: |
Electronic resources
: Monograph/item
|
| Title/Author: |
Computer vision - ECCV 2024 Workshops/ edited by Alessio Del Bue ... [et al.]. |
| Reminder of title: |
Milan, Italy, September 29-October 4, 2024 : proceedings. |
| remainder title: |
ECCV 2024 Workshops |
| other author: |
Del Bue, Alessio. |
| corporate name: |
European Conference on Computer Vision |
| Published: |
Cham :Springer Nature Switzerland : : 2025., |
| Description: |
lv, 408 p. :ill. (some col.), digital ;24 cm. |
| [NT 15003449]: |
Landmark-Based Screening: Femoral Head Coverage and Graf Classification in 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 Detection Datasets. -- Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models. -- A semiotic methodology for assessing the compositional effectiveness of generative text-to-image models (Midjourney and DALLoE). -- A Framework for Critical Evaluation of Text-to-Image Models: Integrating Art Historical Analysis, Artistic Exploration, and Critical Prompt Engineering. -- Civiverse: A Dataset for Analyzing User Engagement with Open-Source TTI-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 Movement Assessment. -- FaceOracle: Chat with a Face Image Oracle. -- Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors. -- How to Squeeze An Explanation Out of Your Model. -- How were you created? Explaining synthetic face images generated by diffusion models. -- Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain. -- How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models. -- Architecture-Agnostic Unsupervised Gradient Regularization For Parameter-Efficient Transfer Learning. -- Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution. -- Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis. -- Improved Baselines for Data-efficient Perceptual Augmentation of LLMs. -- Watt for What: Rethinking Deep Learning's Energy-Performance Relationship. |
| Contained By: |
Springer Nature eBook |
| Subject: |
Computer vision - Congresses. - |
| Online resource: |
https://doi.org/10.1007/978-3-031-92089-9 |
| ISBN: |
9783031920899 |