11 papers
Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training
Junwon Ko, Dong-Jae Lee, Minchan Kwon +2
LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for preserving multiple successful…
Continuous Audio Thinking for Large Audio Language Models
Gyojin Han, Dong-Jae Lee, Changho Choi +2
Large audio language models (LALMs) have shown impressive capabilities on diverse audio understanding tasks, ranging from speech transcription to music analysis. However, because L…
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Dong-Jae Lee, Sunghyun Baek, Junmo Kim
Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Ex…
Frequency-Aware Token Reduction for Efficient Vision Transformer
Dong-Jae Lee, Jiwan Hur, Jaehyun Choi +2
Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a s…
SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration
Jongsuk Kim, Jaeyoung Lee, Gyojin Han +3
Recent advancements in deep learning and the availability of high-quality real-world driving datasets have propelled end-to-end autonomous driving. Despite this progress, relying s…
B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding
Changho Choi, Youngwoo Shin, Gyojin Han +2
Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds provide precise spatial geometr…
DAM: Domain-Aware Module for Multi-Domain Dataset Condensation
Jaehyun Choi, Gyojin Han, Dong-Jae Lee +2
Dataset Condensation (DC) has emerged as a promising solution to mitigate the computational and storage burdens associated with training deep learning models. However, existing DC…
Self-supervised Transformation Learning for Equivariant Representations
Jaemyung Yu, Jaehyun Choi, Dong-Jae Lee +2
Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations l…
AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation Model for Open Compound Domain Adaptation
Jaehyun Choi, Junwon Ko, Dong-Jae Lee +1
Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, t…
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
Jiwan Hur, Dong-Jae Lee, Gyojin Han +3
Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. How…
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning
Jiwan Hur, Jaehyun Choi, Gyojin Han +2
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream t…