4 accepted papers
Pre-trained vision-language models (PVLMs) and pseudo-labeling have proven effective in open-vocabulary object detection (OVD). However, when PVLMs trained on image-text data are adapted for OVD tasks, they often encounter challenges with region-text misalignment, resulting in low-quality pseudo-lab…
Substantial quantity and high quality are the golden rules of making a good training dataset with sample privacy protection equally important. Generating synthetic samples that resemble high-quality private data while ensuring Differential Privacy (DP), a formal privacy guarantee, promises scalabili…
Data replay and knowledge distillation are the most effective techniques for mitigating catastrophic forgetting in incremental object detection (IOD). Despite the recent achievements of IOD, there has been limited exploration regarding active learning of previously learned data, model plasticity, an…
Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on memory-constrained devices is challenging due to the large parameter size. Typical solutions such as memory swapping or ex…