1 citations · 2 across the 6 of their papers we have counts for
7 papers
HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding
Ji Ha Jang, Hayeon Kim, Chulwon Lee +2
CLIP (Contrastive Language-Image Pre-training) has become a de facto paradigm for image-text alignment, but it struggles with long-context descriptions (>77 tokens) due to absolute…
Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
Hayeon Kim, Ji Ha Jang, Junghun James Kim +1
While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or…
Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning
Junghun Kim, Ka Hyun Park, Hoyoung Yoon +1
Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities…
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations
Junghun Kim, Jihie Kim
Multimodal sentiment analysis has become an increasingly popular research area as the demand for multimodal online content is growing. For multimodal sentiment analysis, words can…
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms
Junghun Kim, Yoojin An, Jihie Kim
Attention has become one of the most commonly used mechanisms in deep learning approaches. The attention mechanism can help the system focus more on the feature space's critical re…
Representation Learning with Graph Neural Networks for Speech Emotion Recognition
Junghun Kim, Jihie Kim
Learning expressive representation is crucial in deep learning. In speech emotion recognition (SER), vacuum regions or noises in the speech interfere with expressive representation…
One-Shot Item Search with Multimodal Data
Jonghwa Yim, Junghun James Kim, Daekyu Shin
In the task of near similar image search, features from Deep Neural Network is often used to compare images and measure similarity. In the past, we only focused visual search in im…