15 papers
Learning Probabilistic Prompt for Continual Learning
Hyekang Park, Sanghoon Lee, Geon Lee +2
Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge. Prompt-based c…
Improving Visual Token Reduction via Rectifying Distortions for Efficient Multimodal LLM Inference
Hyeonwoo Cho, Donghyeon Baek, Yewon Kim +1
Recent advancements in Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks, yet the quadratic computational complexity arising from t…
Shift-and-Sum Quantization for Visual Autoregressive Models
Jaehyeon Moon, Bumsub Ham
Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains re…
Jailbreaking on Text-to-Video Models via Scene Splitting Strategy
Wonjun Lee, Haon Park, Doehyeon Lee +2
Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While recent studies have explored vulnerabilit…
TAS-LoRA: Transformer Architecture Search with Mixture-of-LoRA Experts
Jeimin Jeon, Hyunju Lee, Bumsub Ham
Transformer architecture search (TAS) discovers optimal vision transformer (ViT) architectures automatically, reducing human effort to manually design ViTs. However, existing TAS m…
Exploring Hierarchical Consistency and Unbiased Objectness for Open-Vocabulary Object Detection
Sanghoon Lee, Geon Lee, Hyekang Park +1
Conventional object detectors typically operate under a closed-set assumption, limiting recognition to a predefined set of base classes seen during training. Open-vocabulary object…