3 accepted papers
Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.g., 2, 3-bit), creating a "deployment gap" where LLMs cannot be optimally fitted to specific memory budgets. To bridge this gap, we introduce LiftQuant, a novel framework that enables continuous bit-width c…
3D single object tracking (SOT) remains a highly challenging task due to the inherent crux in learning representations from point clouds to effectively capture both spatial shape features and temporal motion features. Most existing methods employ a category-specific optimization paradigm, training t…
3D semantic occupancy prediction is a crucial task in visual perception, as it requires the simultaneous comprehension of both scene geometry and semantics. It plays a crucial role in understanding 3D scenes and has great potential for various applications, such as robotic vision perception and auto