3 accepted papers
While large-scale reasoning models have achieved remarkable performance gains by scaling test-time computation through extended Chain-of-Thought (CoT) sequences, their practical utility is severely constrained by protracted inference lengths and high computational latency. In this paper, we present
The Segment Anything Model (SAM) has demonstrated strong versatility across various visual tasks. However, its large storage requirements and high computational cost pose challenges for practical deployment. Post-training quantization (PTQ) has emerged as an effective strategy for efficient deployme…
Large Multimodal Models (LMMs) are increasingly essential in various real-time applications, yet their substantial parameter counts and complex architectures pose significant challenges. Traditional global compression methods often rely on trial-and-error experimentation, leading to inefficiencies.…