6 accepted papers
High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequ
Recent class-incremental learning (CIL) methods built on large pre-trained vision models have shown that strong performance can be retained even under strict data access constraints. This raises a fundamental question: which properties of pre-trained representations make such recovery possible in th…
Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images h…
Multi-object images are widely present in the real world, spanning various areas of daily life. Efficient and accurate editing of these images is crucial for applications such as augmented reality, advertisement design, and medical imaging. Stable Diffusion (SD) has ushered in a new era of high-qual…
Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance difference between two images domains. To tackle these p…
Recent studies on image patch matching are paying more attention on hard sample learning, because easy samples do not contribute much to the network optimization. They have proposed various hard negative sample mining strategies, but very few addressed this problem from the perspective of loss funct…