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Found 4 papers, 2 papers with code
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation
Originating from the diffusion phenomenon in physics, which describes the random movement and collisions of particles, diffusion generative models simulate a random walk in the data space along the denoising trajectory.
CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
To address this gap, we introduce CoMM, a high-quality Coherent interleaved image-text MultiModal dataset designed to enhance the coherence, consistency, and alignment of generated multimodal content.
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
The boom of Generative AI brings opportunities entangled with risks and concerns.
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved.
