21 citations · 22 across the 10 of their papers we have counts for
10 papers
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao +3
Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent st…
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…
Learning Where It Matters: Geometric Anchoring for Robust Preference Alignment
Youngjae Cho, Jongsuk Kim, Ji-Hoon Kim
Direct Preference Optimization (DPO) and related methods align large language models from pairwise preferences by regularizing updates against a fixed reference policy. As the poli…
FxSearcher: gradient-free text-driven audio transformation
Hojoon Ki, Jongsuk Kim, Minchan Kwon +1
Achieving diverse and high-quality audio transformations from text prompts remains challenging, as existing methods are fundamentally constrained by their reliance on a limited set…
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…
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition
Jongsuk Kim, Jaemyung Yu, Minchan Kwon +1
Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-worl…
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
Minchan Kwon, Gaeun Kim, Jongsuk Kim +2
Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) has expanded. Reinforcement Learning (RL) is widely used…
AVCap: Leveraging Audio-Visual Features as Text Tokens for Captioning
Jongsuk Kim, Jiwon Shin, Junmo Kim
In recent years, advancements in representation learning and language models have propelled Automated Captioning (AC) to new heights, enabling the generation of human-level descrip…
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho +2
Recent advancements in self-supervised audio-visual representation learning have demonstrated its potential to capture rich and comprehensive representations. However, despite the…
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
Janghyeon Lee, Jongsuk Kim, Hyounguk Shon +4
Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream a…