3 accepted papers
Post-training quantization (PTQ) with computational equivalence for Large Language Models (LLMs) have demonstrated remarkable advances, however, their application to Multimodal Large Language Models (MLLMs) presents substantial challenges. In this paper, we analyze SmoothQuant as a case study and id
The integration of RGB and depth modalities significantly enhances the accuracy of segmenting complex indoor scenes, with depth data from RGB-D cameras playing a crucial role in this improvement. However, collecting an RGB-D dataset is more expensive than an RGB dataset due to the need for specializ…
Monocular video depth estimation is a key challenge in computer vision, highlighting its importance in visual understanding. Monocular depth estimation models trained on single images achieve impressive results on individual frames but often lack temporal consistency when applied to videos, leading