Leaping Into Memories: Space-Time Deep Feature Synthesis

• URL: http://arxiv.org/abs/2303.09941v4
• Date: Tue, 25 Jul 2023 12:24:47 GMT
• Title: Leaping Into Memories: Space-Time Deep Feature Synthesis
• Authors: Alexandros Stergiou and Nikos Deligiannis
• Abstract summary: We propose LEAPS, an architecture-independent method for synthesizing videos from internal models. We quantitatively and qualitatively evaluate the applicability of LEAPS by inverting a range of architectures convolutional attention-based on Kinetics-400.
• Score: 93.10032043225362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of deep learning models has led to their adaptation and adoption by prominent video understanding methods. The majority of these approaches encode features in a joint space-time modality for which the inner workings and learned representations are difficult to visually interpret. We propose LEArned Preconscious Synthesis (LEAPS), an architecture-independent method for synthesizing videos from the internal spatiotemporal representations of models. Using a stimulus video and a target class, we prime a fixed space-time model and iteratively optimize a video initialized with random noise. Additional regularizers are used to improve the feature diversity of the synthesized videos alongside the cross-frame temporal coherence of motions. We quantitatively and qualitatively evaluate the applicability of LEAPS by inverting a range of spatiotemporal convolutional and attention-based architectures trained on Kinetics-400, which to the best of our knowledge has not been previously accomplished.

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