5 accepted papers
The rapid progress in generative models has significantly enhanced the quality of image generation. However, as these models grow larger, deploying and fine-tuning them becomes increasingly challenging. While conventional quantization techniques help reduce model size, they struggle to achieve high…
Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But few-shot compression for…
This paper introduces a novel pipeline for long-tail (LT) recognition that diverges from conventional strategies. Instead, it leverages the long-tailed dataset itself to generate a balanced proxy dataset without utilizing external data or model. We deploy a diffusion model trained from scratch on on…
Recently, various strategies for distributed training of large language models (LLMs) have been proposed. By categorizing them into basic strategies and composite strategies, we have discovered that existing basic strategies provide limited options in specific scenarios, leaving considerable room fo…
Robotic bronchoscopic intervention requires detailed 3D airway maps for both localisation and enhanced visualisation, especially at peripheral airways. Patient-specific airway maps can be generated from preoperative chest CT scans. Due to pathological abnormalities and anatomical variations, automat…