Posters

Constraining vision-language model tuning via persistent homology ensures consistent image-text alignment, improving few-shot learning and domain generalization.

Hollowed Net efficiently personalizes text-to-image diffusion models on-device by temporarily removing deep U-Net layers during training, drastically reducing memory usage without sacrificing performa…

HOI-Swap: a novel diffusion model flawlessly swaps objects in videos while intelligently preserving natural hand interactions, producing high-quality edits.

HLM-Cite: A hybrid language model workflow boosts scientific citation prediction accuracy by 17.6% and scales to 100K candidate papers, surpassing existing methods.

HisTPT: Historical Test-Time Prompt Tuning memorizes past learning, enabling robust online prompt adaptation for vision models, overcoming performance degradation in continuously changing data streams…

HippoRAG, a neurobiologically inspired framework, dramatically improves LLM long-term memory and multi-hop question answering by synergistically orchestrating LLMs, knowledge graphs, and the Personali…

Higher-rank irreducible Cartesian tensors boost accuracy and efficiency in equivariant message-passing neural networks for atomistic simulations.

Higher-Order Causal Message Passing (HO-CMP) accurately estimates treatment effects in complex systems with unknown interference by using observed data to learn the system’s dynamics over time.

AICT: Adaptive-Interval Color Transformation harmonizes high-resolution images by predicting pixel-wise color changes, adaptively adjusting sampling intervals to capture local variations, and using a …

First high-probability complexity guarantees for solving stochastic nonconvex minimax problems using a single-loop method are established.

Adversarial training achieves minimax-optimal prediction error in high-dimensional linear regression under l∞-perturbation, improving upon existing methods.

High-Rank PCF-GAN uses a novel metric (HRPCFD) based on high-rank path development to learn filtration of stochastic processes, outperforming state-of-the-art methods in hypothesis testing and time-se…

This paper introduces HVFA, a novel OCR-free document understanding framework using MLLMs and multi-scale visual features, achieving superior performance across various document understanding tasks.

Visualizing high-dimensional posterior distributions is challenging. This paper introduces ‘Posterior Trees,’ a novel method using tree-structured neural network predictions for hierarchical uncertai…

Hierarchical Selective Classification (HSC) improves deep learning model reliability for risk-sensitive tasks by leveraging hierarchical class relationships to provide more informative predictions eve…

Hierarchical Programmatic Option framework (HIPO) uses human-readable programs as options in reinforcement learning to solve long, repetitive tasks with improved interpretability and generalization.

Hierarchical Hybrid Sliced Wasserstein (H2SW) solves the challenge of comparing complex, heterogeneous joint distributions by introducing novel slicing operators, leading to a scalable and statistical…

MTGC tackles multi-timescale model drift in hierarchical federated learning.

HiCoM, a novel framework, achieves high-fidelity streamable dynamic scene reconstruction by using a hierarchical coherent motion mechanism and parallel processing to significantly reduce training time…

HiCo: Hierarchical Controllable Diffusion Model achieves superior layout-to-image generation by disentangling spatial layouts through a multi-branch network structure, resulting in high-quality images…