8 accepted papers
Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality content generation with substantially fewer decoding steps. However, existing VAR models suffer from significant attention complexity and severe memory overhead due to the accumulation of key-value (KV)
Exploration in cooperative multi-agent reinforcement learning (MARL) remains challenging for value-based agents due to the absence of an explicit policy. Existing approaches include individual exploration based on uncertainty towards the system and collective exploration through behavioral diversity…
Mathematical reasoning remains an ongoing challenge for AI models, especially for geometry problems, which require both linguistic and visual signals. As the vision encoders of most MLLMs are trained on natural scenes, they often struggle to understand geometric diagrams, performing no better in geo…
In handling complex, domain-specific tasks, particularly in the context of state-owned assets and enterprises (SOAEs), general LLMs suffer from the knowledge gap due to insufficient exposure to domain-specific corpora, and the value disagreement, as they are aligned with universal values rather than…
Combining face-swapping with lip synchronization offers a cost-effective solution for generating customized talking faces. However, directly cascading existing models can introduce significant interference and reduce video clarity due to limited interaction space in the low-level RGB domain. To solv…
Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During the centralized training, agents can be guided by the same signals, such as the global state. However, agents lack the shared signal and ch…
Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning. However, almost all existing methods follow the principle of Individual Global Max (IGM) or its variants, which limits their problem-solving capabilities. To address this, we propose a…
Point cloud classification has sparked many researchers’ interest for its cornerstone role in 3D applications. Inheriting the CutMix series augmentation that performs well in 2D images, PointCutMix and RSMix are proposed to generate new samples for 3D point clouds, by replacing partial points of one…