ECCV 2024 Advances in Image Manipulation (AIM) workshop and challenges
July 10th, 2024 
Daniela Lopez de Luise In conjunction with ECCV 2024, Milano, Italy
Website: https://www.cvlai.net/aim/2024/
Contact: radu.timofte@uni-wuerzburg.de
TOPICS
Papers addressing topics related to image/video manipulation, restoration and enhancement are invited. The topics include, but are not limited to:
● Video-to-video translation
● Image/video manipulation
● Perceptual manipulation
● Image/video generation and hallucination
● Image/video quality assessment
● Image/video semantic segmentation
● Multimodal translation
● Depth estimation
● Image/video inpainting
● Image/video deblurring
● Image/video denoising
● Image/video upsampling and super-resolution
● Image/video filtering
● Image/video de-hazing, de-raining, de-snowing, etc.
● Demosaicing
● Image/video compression
● Removal of artifacts, shadows, glare and reflections, etc.
● Image/video enhancement: brightening, color adjustment, sharpening, etc.
● Style transfer
● Hyperspectral imaging
● Underwater imaging
● Aerial and satellite imaging
● Methods robust to changing weather conditions / adverse outdoor conditions
● Image/video manipulation on mobile devices
● Image/video restoration and enhancement on mobile devices
● Studies and applications of the above.
SUBMISSION
A paper submission has to be in English, in pdf format, and at most 14 pages (excluding references) in single-column, ECCV style. The paper format must follow the same guidelines as for all ECCV 2024 submissions.
The review process is double blind.
Dual submission is not allowed.
Submission site: https://cmt3.research.microsoft.com/AIMWC2024/
WORKSHOP DATES
● Decisions: August 7, 2024
● Camera Ready Deadline: August 14, 2024
AIM 2024 has the following associated challenges (ONGOING!):
● Mobile Real-Time Video Super-Resolution
● Efficient Video Super-Resolution
● Depth Upsampling and Refinement
● RAW Burst Alignment and ISP ongoing
● Sparse Neural Rendering – Track 1 – 3 views
● Sparse Neural Rendering – Track 2 – 9 views
● Video Saliency Prediction
● Video Super-Resolution Quality Assessment
● Compressed Video Quality Assessment
● Pushing the Boundaries of Blind Photo Quality Assessment
To learn more about the challenges and to participate:
● Competitions end: July 19, 2024
Email: radu.timofte@uni-wuerzburg.de
Website: https://www.cvlai.net/aim/2024/
1st Workshop on Trustworthiness in Computer Vision “TWYN: Trust What You learN”
July 10th, 2024 Daniela Lopez de Luise
Scope:
In an era of rapid advancements in Artificial Intelligence, the imperative to foster Trustworthy AI has never been more critical. The first “Trust What You learN (TWYN)'' workshop seeks to create a dynamic forum for researchers, practitioners, and industry experts to explore and advance the intersection of Trustworthy AI and DeepFake Analysis within the realm of Computer Vision. The workshop aims to delve into the multifaceted dimensions of building AI systems that are not only technically proficient but also ethical, transparent, and accountable. The dual focus on Trustworthy AI and DeepFake Analysis reflects the workshop's commitment to addressing the challenges posed by the proliferation of deep fake technologies while simultaneously promoting responsible AI practices. The workshop will include several talks presented by recognized and important scientists in these 2 fields. Explore the detailed information about our workshop structure, esteemed confirmed speakers, and what to expect on the website of the workshop.
Tracks:
Track 1: From Learning to Unlearning: The Role of Privacy in Computer Vision – Dive into privacy-centric topics like differential privacy, privacy attacks, Machine Unlearning and more!
Differential privacy
Statistical and information-theoretic notions of privacy
Privacy-preserving data sharing, anonymization, privacy of synthetic data and distillation
Privacy attacks
Federated and decentralized privacy-preserving algorithms
Privacy and bias correction in generative models
Privacy in autonomous systems
Privacy and private learning in computer vision and natural language processing tasks
Relations of privacy with fairness, transparency and adversarial robustness
Machine unlearning and data-deletion
Privacy-preserving continual learning systems
Approaches for fake image detection, relying on both low-level, hand-crafted features or learnable and semantic approaches
Partially-altered fake image detection
GAN and Diffusion-based techniques with safety reassurance for image and video synthesis and generation
Video Deepfake detection and multimodal approaches to deep-fake detection
Approaches for detecting generated text and fake news, also based on multimodal analysis
Approaches and techniques for explainable deep-fake detection
Evaluation metrics for deep-fake generation and detection systems
– Dimosthenis Karatzas
– Reza Shokri
– Stefano Soatto
– Rita Cucchiara
– Luisa Verdoliva
Paper submissions:
Paper submission instructions are available at https://www.twyn.unimore.it/call-for-papers/. Accepted papers will be presented at the workshop posters session and included in the proceedings. The submission deadline has been postponed from the 10th to the 19th 2024 of July. Don't miss out on this opportunity to contribute to the discussion!
Dates:
Paper Submission Deadline: July 10 July 19, 2024 AoE
Supplementary Material Deadline: 10 July 19 July, 2024 AoE
Workshop date: 30 September, 2024
Submission Website: https://cmt3.research.microsoft.com/TWYN2024/
Organizers:
– Marco Cotogni, Leonardo Spa
– Luigi Sabetta, Leonardo Spa
– Jacopo Bonato, Leonardo Spa
– Sara Sarto, UniMoRe
– Samuele Poppi, UniMoRe
– Lorenzo Baraldi, UniMoRe
ECCV Workshop: Towards Multimodal Foundational Models for Modelling Visual Cortex
July 10th, 2024 Daniela Lopez de Luise -
Theoretical Frameworks and Computational Approaches: Novel theoretical constructs and computational strategies for modeling the visual cortex using multimodal data.
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Integration of Diverse Data Sources: Techniques and challenges in integrating and harmonizing heterogeneous data modalities such as fMRI, EEG, in vivo two-photon calcium imaging, fNIRS, and others.
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Learning Paradigms for Noisy Data: Innovations in learning algorithms and paradigms to effectively handle noisy and incomplete data in modeling brain functions.
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Applications in Neuroscientific Research: Practical applications of multimodal foundational models in elucidating perception, cognition, and neurodevelopmental or neurodegenerative disorders.
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Contrastive Learning for Multimodal Brain Data Fusion: Techniques and advancements in leveraging contrastive learning methods to fuse multimodal brain data for enhanced representation and analysis.
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Self-Supervised Learning for Temporal Brain Dynamics: Approaches utilizing self-supervised learning to capture and model temporal dynamics in brain imaging and physiological data.
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Unsupervised Learning for Structural and Functional Brain Network Construction: Methods employing unsupervised learning to construct and analyze structural and functional brain networks from multimodal data.
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Weakly Supervised Learning for Brain Connectivity Analysis: Innovations in weakly supervised learning techniques for analyzing brain connectivity patterns and networks.
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Foundational Models for Classification and Predictive Modeling: Development and application of foundational models for classification and predictive modeling tasks in neuroscience.
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Multimodal Brain Image Visualization with Advanced Learning Techniques: Techniques for visualizing multimodal brain images using advanced learning and visualization methods to aid in data interpretation.
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Ethical Implications: Ethical considerations in the creation, use, and implications of foundational models in neuroscience research and applications.
Synthetic Realities and Biometric Security: Advances in Forensic Analysis and Threat Mitigation (SRBS)
July 10th, 2024 Daniela Lopez de Luise Advances in Forensic Analysis and Threat Mitigation (SRBS)
Workshop held at the 2024 British Machine Vision Conference (BMVC)
27-28 November 2024, Glasgow, UK
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*** Call for Papers ***
Recent advancements in deep learning algorithms, such as Generative Adversarial Networks (GANs) and Diffusion models, have led to a surge in the creation of highly realistic images and videos, which are often indistinguishable from genuine content to the human eye. While these developments have benefited the entertainment industry, they are often used to spread misinformation and manipulate public opinion. Moreover, in the security domain, synthetic and manipulated images and videos are frequently utilized to impersonate individuals, enabling unauthorized parties to bypass systems for biometric authentication gaining illegal access to sensitive information. Accurate detection of fake data is crucial for preventing security breaches and safeguarding the integrity of security measures and public information.
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Human-oriented generative models and image/video synthesis techniques,
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Detection of tempered, manipulated and synthetic content,
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Deepfake generation and detection,
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Morphing attack generation and detection,
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Image manipulation attacks in biometrics verification and identification scenarios,
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Video manipulation attacks,
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Physical (presentation) attacks on biometric systems,
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Forensic analysis of behavioral biometrics,
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Ethical and societal implications of manipulation techniques,
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Synthetic realities,
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Relevant case studies and applications,
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New datasets and performance benchmarks,
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Watermarking.
*** Submission ***
*** Important Dates ***
Full Paper Submission: August 4, 2024, 11:59pm PST
Acceptance Notice: August 16, 2024, 11:59pm PST