| 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, 454 p. :ill. (some col.), digital ;24 cm. |
| [NT 15003449]: |
FALCON: Fair Active Learning for Content Moderation -- Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria -- Beyond the Surface: A Comprehensive Analysis of Implicit Bias in Vision-Language Models -- Fairness of AI Systems in the Legal Context -- DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model -- Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition -- Prompt and Prejudice -- Localization-Guided Supervision for Robust Medical Image Classification by Vision Transformers -- Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation -- Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers -- An Investigation on The Position Encoding in Vision-Based Dynamics Prediction -- What could go wrong? Discovering and describing failure modes in computer vision -- Image-guided topic modeling for interpretable privacy classification -- Integrating Local and Global Interpretability for Deep Concept-Based Reasoning Models -- From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation -- Feature Contribution in Monocular Depth Estimation -- Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go? -- Explanation Alignment: Quantifying the Correctness of Model Reasoning At Scale -- Detect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detection -- Incremental and Decremental Continual Learning for Privacy-preserving Video Recognition -- Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake Detection -- Are CLIP features all you need for Universal Synthetic Image Origin Attribution? -- GLoFool: global enhancements and local perturbations to craft adversarial images -- Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images -- Your diffusion model is an implicit synthetic image detector -- The Phantom Menace: Unmasking Privacy Leakages in Vision-Language Models. |
| Contained By: |
Springer Nature eBook |
| Subject: |
Computer vision - Congresses. - |
| Online resource: |
https://doi.org/10.1007/978-3-031-92648-8 |
| ISBN: |
9783031926488 |