4 accepted papers
Retrieval-augmented generation (RAG) has greatly improved Large Language Models (LLMs) by adding external knowledge. However, current RAG-based methods face difficulties with long-context video understanding due to two main challenges. First, Current RAG-based methods for long-context video understa
Long-video temporal reasoning remains challenging for Large Video Language Models (LVLMs). Recent reasoning-enhanced models apply reinforcement learning with outcome supervision to improve temporal understanding. However, outcome-only rewards cannot distinguish whether a model arrived at the correct…
In Multiple Instance Learning (MIL) for Whole Slide Image (WSI) analysis, attention mechanisms are often employed to weigh the importance of different instances. However, global attention may lead to feature homogenization and overlook tissue differences. Adding local attention can capture these var…
Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) t…