🏢 Nankai University

Zipper: A novel statistical device resolves the degeneracy issue in algorithm-agnostic inference, enabling reliable goodness-of-fit tests with enhanced power.

ToMe: a novel training-free method dramatically improves semantic binding in text-to-image synthesis by intelligently merging related tokens, ensuring accurate alignment between generated images and t…

This paper introduces novel metrics for visual emotion recognition evaluation, considering the psychological distance between emotions to better reflect human perception, improving the assessment of m…

StoryDiffusion enhances long-range image & video generation by introducing a simple yet effective self-attention mechanism and a semantic motion predictor, achieving high content consistency without t…

SARDet-100K: A new benchmark dataset and open-source toolkit revolutionizes large-scale SAR object detection.

II-COS: a novel online sample selection method effectively controls individual and interactive constraints in real-time via predictive inference, improving efficiency and addressing various practical …

OPUS: a novel, real-time occupancy prediction framework using a sparse set prediction paradigm, outperforms state-of-the-art methods on Occ3D-nuScenes.

LE3D: Real-time HDR view synthesis from noisy RAW images is achieved using 3DGS, significantly reducing training time and improving rendering speed.

Grid4D: A novel 4D decomposed hash encoding boosts high-fidelity dynamic Gaussian splatting, surpassing state-of-the-art models in visual quality and rendering speed.

Faster Diffusion achieves significant speedups in diffusion model inference by cleverly reusing encoder features and enabling parallel processing, eliminating the need for computationally expensive di…

This paper introduces Selective Conformal P-Value (SCPV), a novel method for controlling FDR in conformalized multiple testing after data-dependent selection, offering a unified theoretical framework …

AlphaPruning leverages Heavy-Tailed Self-Regularization theory to allocate optimal layer-wise sparsity ratios in LLMs, achieving 80% sparsity in LLaMA-7B with reasonable perplexity.