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We observe an even stronger tendency for Swin to conserve initial predictions under partial occlusion. We show our experiment in Figure 2. We find very similar conclusions, ConvNext to object features in the canonical pose. Here we present more details about the proposed NVD dataset. Next, in Figure 1, we present a non-exhaustive showcase of the 92 object models contained in NVD. Unfortunately, Swin V2 architectures are exclusively available for inference on images of size at least 256x256.

Whereas generative adversarial networks are capable of synthesizing highly realistic images of faces, cats, landscapes, or almost any other single category, paint-by-text synthesis engines can -- from a single text prompt -- synthesize realistic images of seemingly endless categories with arbitrary configurations and combinations. This powerful technology poses new challenges to the photo-forensic community. Motivated by the fact that paint by text is not based on explicit geometric or physical models, and the human visual system's general insensitivity to lighting inconsistencies, we provide an initial exploration of the lighting consistency of DALL-E-2 synthesized images to determine if physics-based forensic analyses will prove fruitful in detecting this new breed of synthetic media.