Posters

Boosting indoor panoramic semantic segmentation, a new approach leverages geometric properties to optimize over- and under-sampled image segments for improved accuracy and robustness.

Guaranteed Manifold Clustering: Novel method provides geometric conditions ensuring accurate data grouping from nonlinear manifolds, showing competitive performance on CIFAR datasets.

GeoLRM: Generate stunning 3D models from just 21 images using a novel geometry-aware transformer, surpassing existing methods in efficiency and quality!

GenWarp generates high-quality novel image views from a single input image by using a semantic-preserving generative warping framework, outperforming existing methods.

GenRL: Learn diverse embodied tasks from vision & language, without reward design, using multimodal imagination!

GenRec: One diffusion model to rule both video generation and recognition!

GENOT: a flexible neural optimal transport framework for single-cell genomics, enabling stochastic map learning with any cost function, handling unbalanced data, and tackling complex (Fused) Gromov-Wa…

Genetic-guided GFlowNets revolutionize sample-efficient molecular optimization by smartly integrating genetic algorithms into GFlowNets training, achieving state-of-the-art performance with substantia…

GGAD: Generative Semi-supervised Graph Anomaly Detection significantly outperforms existing methods by using a novel approach to generate pseudo-anomaly nodes for training, leveraging asymmetric local…

ConStruct: Generating realistic graphs with guaranteed structural properties via constrained diffusion.

MDGEN: Generative modeling unlocks MD data for diverse tasks, achieving significant speedups via flexible multi-task surrogate models.

Generative Hierarchical Materials Search (GenMS) uses AI to design novel crystal structures from natural language descriptions, outperforming prior methods in both fulfilling user requests and finding…

Generative Fractional Diffusion Models (GFDM) leverages fractional diffusion processes for superior image generation, enhancing diversity and quality while addressing existing diffusion model limitati…

Generative Forests (GFs) revolutionize tabular data generation with a novel forest-based model and a simple boosting algorithm offering strong convergence guarantees, significantly outperforming curre…

Generative adversarial model-based optimization via adaptive source critic regularization (aSCR) significantly boosts offline optimization accuracy.

GeNSIT: a neural framework efficiently generates origin-destination matrices for agent-based models, outperforming existing methods in accuracy and scalability by directly operating on the discrete sp…

This paper introduces a novel multi-stage generation framework for creating compositional scenes with fine-grained control by leveraging a trained diffusion model to produce individual scene component…

LLMs guided by Monte Carlo Tree Search generate precise, efficient Python code as world models for model-based reinforcement learning, significantly improving sample efficiency and inference speed.

Researchers developed FSAFW, a novel framework generating universal adversarial perturbations effective against various Few-Shot Learning paradigms, surpassing baseline methods by over 16%.

WeatherGFT generalizes weather forecasts to finer temporal scales using a physics-AI hybrid model, achieving state-of-the-art performance and 30-minute forecast capability with only hourly training da…