Posters

Researchers break the curse of dimensionality in structured density estimation using graph resilience, a novel graphical parameter that effectively reduces the sample complexity.

Researchers developed a new AI-generated image detection method that overcomes the limitation of existing detectors, achieving superior cross-scene generalization by shuffling image patches and traini…

DDSR: a novel sequential recommendation model uses fuzzy sets and discrete diffusion to capture user behavior randomness, outperforming existing methods.

BrainBits reveals that surprisingly little brain information is needed for high-fidelity image & text reconstruction, highlighting the dominance of generative model priors over neural signal extractio…

This paper delivers novel, universally applicable bounds for the smallest NTK eigenvalue, regardless of data distribution or dimension, leveraging the hemisphere transform.

Bi-Level Active Finetuning Framework (BiLAF) revolutionizes sample selection for efficient model finetuning. Unlike existing methods, BiLAF incorporates both global diversity and local decision bounda…

BDNE: a novel boundary decomposition method accurately estimates the nadir objective vector in complex multi-objective optimization problems.

This paper introduces a novel object-centric learning (OCL) framework that enhances slot attention with a self-modulating top-down pathway, significantly improving object representation and achieving …

PCNet boosts weakly-supervised referring image segmentation by progressively processing textual cues, mimicking human comprehension, and significantly improving target localization.

ACON: Adversarial CO-learning Networks enhances time series domain adaptation by cleverly combining temporal and frequency features. Frequency features boost within-domain discriminability, while temp…

Boosting vision transformer adversarial attack transferability, this paper introduces Adaptive Token Tuning (ATT), improving attack success rate by 10.1% over existing methods.

Boosting LLMs with an intelligent information assistant, ASSISTRAG, significantly improves accuracy and reasoning, especially for less advanced models.

MLLMs enhance text-to-video generation by providing 135k fine-grained video preferences, creating VIDEOPREFER, and a novel reward model, VIDEORM, boosting video quality and alignment.

ViSu boosts semi-supervised scene text recognition by using an online generation strategy for diverse synthetic data and a novel character alignment loss to improve model generalization and robustness…

Equivariant Graph Neural Networks boost multi-agent reinforcement learning by improving sample efficiency and generalization, overcoming inherent exploration biases.

Boosting graph neural networks: Topology-Invariant Pooling (TIP) leverages persistent homology to enhance graph pooling, achieving consistent performance gains across diverse datasets.

GEPS enhances parametric PDE solver generalization by using adaptive conditioning, achieving superior performance with limited data.

This research introduces a novel framework for post-unlearning in text-to-image generative models, optimizing model updates to ensure both effective forgetting and maintained text-image alignment.

Boosting conformal prediction intervals improves accuracy and precision by tailoring them to specific desired properties via machine learning.

BoostAdapter enhances vision-language model test-time adaptation by combining instance-agnostic historical samples with instance-aware boosting samples for superior out-of-distribution and cross-domai…