6 accepted papers
3D Gaussian Splatting (3DGS) has received tremendous popularity over the past few years due to its photorealistic visual appearance. However, 3DGS uses volumetric rendering that is not suitable for objects with non-lambertian or transparent materials. To remedy this issue, a family of Order-Independ
Current multimodal large language models (MLLMs) face significant challenges in visual document understanding (VDU) tasks due to the high resolution, dense text, and complex layouts typical of document images. These characteristics demand a high level of detail perception ability from MLLMs. While i…
In deep reinforcement learning (DRL), the presence of dormant neurons leads to a significant reduction in network capacity, which results in sub-optimal performance and limited sample efficiency. Existing training techniques, especially those relying on periodic resetting (PR), exacerbate this issue
Online continual learning (OCL) is a strict setting of continual learning (CL), where the OCL agent faces a never-ending data stream and encounters each new sample only once. An OCL agent suffers more serious catastrophic forgetting (i.e., forgetting previous knowledge of old classes) than a CL agen…
Catastrophic forgetting has become an intractable problem in the continual learning setting because previous data is not accessible when training. To mitigate this problem, memory-based continual learning methods replay previous data from a fixed-size memory buffer. Reservoir sampling, which can sam…
A class-incremental learning agent learns online with a neverending stream of data in one training epoch. In this setting, the agent suffers from severe catastrophic forgetting due to the absence of data from the observed classes after learning data from new classes. Besides, the prototypes rapidly…