JSCGC replaces the conventional decoder with a generative model for controlled sampling, reformulating communication as mutual information maximization under perceptual constraints and showing improved semantic and distributional quality in image transmission experiments.
Rethinking fid: Towards a better evaluation metric for image generation
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FlowWM trains a flow-matching model in frozen DINOv3 feature space, using a one-step projection for temporal and task-driven losses, improving stochastic future prediction on a Waymo-derived benchmark.
Prompt-generated image-mask pairs, mixed with real UAV imagery at a 40:60 ratio, lift forest-regeneration segmentation by >15 F1 points over supervised baselines and sharply improve rare-species F1.
Seedream 2.0 is a native Chinese-English bilingual diffusion model that integrates a self-developed LLM text encoder, Glyph-Aligned ByT5, and Scaled ROPE to reach claimed state-of-the-art results in prompt following, aesthetics, text rendering, and human preference alignment via RLHF.
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
citing papers explorer
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JSCGC: Joint Source-Channel-Generation Coding for Wireless Generative Communications
JSCGC replaces the conventional decoder with a generative model for controlled sampling, reformulating communication as mutual information maximization under perceptual constraints and showing improved semantic and distributional quality in image transmission experiments.
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Flow Matching in Feature Space for Stochastic World Modeling
FlowWM trains a flow-matching model in frozen DINOv3 feature space, using a one-step projection for temporal and task-driven losses, improving stochastic future prediction on a Waymo-derived benchmark.
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Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
Prompt-generated image-mask pairs, mixed with real UAV imagery at a 40:60 ratio, lift forest-regeneration segmentation by >15 F1 points over supervised baselines and sharply improve rare-species F1.
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Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
Seedream 2.0 is a native Chinese-English bilingual diffusion model that integrates a self-developed LLM text encoder, Glyph-Aligned ByT5, and Scaled ROPE to reach claimed state-of-the-art results in prompt following, aesthetics, text rendering, and human preference alignment via RLHF.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.