On generating image and video hallucinations - presented by Prof Sabine Süsstrunk

On generating image and video hallucinations

Prof Sabine Süsstrunk

SS
On generating image and video hallucinations
SS
Sabine Süsstrunk
École Polytechnique Fédérale de Lausanne

“Hallucination” is a term used in the AI community to describe the plausible falsehoods produced by deep generative neural networks. It is often considered a negative, especially in relation with large language models or medical image reconstruction. Yet, in many computational photography applications, we rely on such hallucinations to create pleasing images. It often does not matter if all (or any) information was present in the real world if the produced falsehoods are visually plausible.

Starting from that premise, I will present our recent work on hallucinations in image reconstruction, image style creation, and texture synthesis, using different generative models such as GANs, diffusion networks, and neural cellular automata. With a nod to the dangers some of these hallucinations might pose, I will also briefly discuss our work on deep fake detection.

References
  • 1.
    R. Zhou and S. Susstrunk (2020) Kernel Modeling Super-Resolution on Real Low-Resolution Images.
  • 2.
    M. E. Helou et al. (2020) Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks. Lecture Notes in Computer Science
  • 3.
    M. E. Helou and S. Susstrunk (2022) BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration. IEEE Transactions on Image Processing
  • 4.
    M. N. Everaert et al. (2024) Diffusion in Style.
  • 5.
    M. N. Everaert et al. (2024) Exploiting the Signal-Leak Bias in Diffusion Models.
  • 6.
    E. Pajouheshgar et al. (2023) DyNCA: Real-Time Dynamic Texture Synthesis Using Neural Cellular Automata.
  • 7.
    E. Pajouheshgar et al. (2024) NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata.
  • 8.
    E. Pajouheshgar et al. (2024) Mesh Neural Cellular Automata. ACM Transactions on Graphics
  • 9.
    P. Grönquist et al. (2024) Efficient Temporally-aware DeepFake Detection using H.264 Motion Vectors. Electronic Imaging
Grants
    Innosuisse - Schweizerische Agentur für Innovationsförderung 48552.1Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCRSII5−180359
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S. Süsstrunk (2024, December 6), On generating image and video hallucinations
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Listed seminar This seminar is open to all
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Video length 58:38
Disclaimer The views expressed in this seminar are those of the speaker and not necessarily those of the journal