ShadeBench is a multimodal benchmark dataset for urban shade understanding that includes temporally varying shade maps, satellite imagery, building representations, and text to support shade generation, segmentation, and 3D reconstruction tasks.
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LEGO uses multiple generator-specific LoRA modules modulated by an MLP and fused with attention to detect synthetic images, achieving better performance than prior methods while using under 10% of the training data.
SHARP applies a spectrum-aware dynamic RoPE scaling schedule that promotes resolution more strongly in early denoising stages and relaxes it later, outperforming static baselines on quality metrics for remote sensing images.
OFlow unifies temporal foresight and object-aware reasoning inside a shared latent space via flow matching to improve VLA robustness in robotic manipulation under distribution shifts.
Creo scaffolds text-to-image generation through progressive stages with editable abstractions and decision locking to improve controllability, agency, and output diversity.
CAGE uses LLM-generated code for label-correct diagrams followed by ControlNet-conditioned diffusion refinement to produce both accurate and visually engaging educational graphics, backed by the new EduDiagram-2K dataset.
InsTraj generates realistic, instruction-faithful GPS trajectories by using an LLM to parse natural-language travel intent and a multimodal diffusion transformer to produce the paths.
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InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories
InsTraj generates realistic, instruction-faithful GPS trajectories by using an LLM to parse natural-language travel intent and a multimodal diffusion transformer to produce the paths.