Posters

Instance-wise LoRA (iLoRA) boosts LLM sequential recommendation accuracy by customizing model parameters for each user, mitigating negative transfer and improving performance.

AI-powered drug interaction prediction gets a boost! CSSE-DDI uses neural architecture search to customize subgraph selection and encoding, resulting in superior accuracy and efficiency compared to e…

Multi-Sub leverages multi-modal learning to achieve customized multiple clustering, aligning user-defined textual preferences with visual representations via a subspace proxy learning framework.

CUFIT: a novel curriculum fine-tuning paradigm significantly improves medical image classification accuracy despite noisy labels by leveraging pre-trained Vision Foundation Models.

CuMo boosts multimodal LLMs by efficiently integrating co-upcycled Mixture-of-Experts, achieving state-of-the-art performance with minimal extra parameters during inference.

CulturePark, a novel multi-agent communication framework, generates high-quality cross-cultural data to fine-tune LLMs, significantly reducing cultural bias and boosting cross-cultural understanding.

CultureLLM, a new approach, effectively incorporates cultural nuances into LLMs using semantic data augmentation, significantly outperforming existing models.

Ctrl-X: Zero-shot text-to-image generation with training-free structure & appearance control!

CSPG: a novel framework boosting Approximate Nearest Neighbor Search speed by 1.5-2x, using sparse proximity graphs and efficient two-staged search.

Score estimation, crucial for diffusion models, is computationally hard even with polynomial sample complexity unless strong distributional assumptions are made.

CryoSPIN revolutionizes ab-initio cryo-EM reconstruction with semi-amortized pose inference, achieving faster and more accurate 3D structure determination.

CryoGEM: Physics-informed generative model creates realistic synthetic cryo-EM datasets, boosting particle picking and pose estimation accuracy for higher-resolution protein structure determination.

CRT-Fusion: Boosting 3D object detection by fusing camera, radar, and motion information for more accurate, robust results!

Cross-video Identity-cOrrelating pre-training (CION) revolutionizes person re-identification by leveraging identity correlation across videos for superior model pre-training, achieving state-of-the-ar…

Self-supervised blind image deblurring (BID) breakthrough! A novel cross-scale consistency loss and progressive training scheme using implicit neural representations achieves superior performance wit…

One-time training improves multiple LLMs using a tiny portable model, drastically reducing costs and resource needs for model enhancement.

Cross-Modality Perturbation Synergy (CMPS) attack: A novel universal perturbation method for cross-modality person re-identification, effectively misleading ReID models by leveraging gradients from di…

Cross-Modal Representation Flattening (CMRF) improves multi-modal domain generalization by creating consistent flat loss regions and enhancing knowledge transfer between modalities, outperforming exis…

CoLA: Collaborative Lifelong Adaptation boosts test-time adaptation efficiency by sharing domain knowledge across multiple devices, achieving significant accuracy gains with minimal computational over…

CRONOS: Scaling convex neural network training to ImageNet!