10 citations · 34 across the 14 of their papers we have counts for
12 papers · 1 filter
Breaking the Visual Shortcuts in Multimodal Knowledge-Based Visual Question Answering
Dosung Lee, Sangwon Jung, Boyoung Kim +4
Existing Multimodal Knowledge-Based Visual Question Answering (MKB-VQA) benchmarks suffer from "visual shortcuts", as the query image typically matches the primary subject entity o…
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval
Boseung Jeong, Jicheol Park, Sungyeon Kim +1
Video-text retrieval, the task of retrieving videos based on a textual query or vice versa, is of paramount importance for video understanding and multimodal information retrieval.…
Efficient and Versatile Robust Fine-Tuning of Zero-shot Models
Sungyeon Kim, Boseung Jeong, Donghyun Kim +1
Large-scale image-text pre-trained models enable zero-shot classification and provide consistent accuracy across various data distributions. Nonetheless, optimizing these models in…
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions
Sohyun Lee, Namyup Kim, Sungyeon Kim +1
Robust semantic segmentation under adverse conditions is crucial in real-world applications. To address this challenging task in practical scenarios where labeled normal condition…
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
Junhyeong Cho, Gilhyun Nam, Sungyeon Kim +2
In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent stu…
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization
Sungyeon Kim, Boseung Jeong, Suha Kwak
Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning…
Cross-Domain Ensemble Distillation for Domain Generalization
Kyungmoon Lee, Sungyeon Kim, Suha Kwak
Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domai…
Self-Taught Metric Learning without Labels
Sungyeon Kim, Dongwon Kim, Minsu Cho +1
We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of…
Learning to Generate Novel Classes for Deep Metric Learning
Kyungmoon Lee, Sungyeon Kim, Seunghoon Hong +1
Deep metric learning aims to learn an embedding space where the distance between data reflects their class equivalence, even when their classes are unseen during training. However,…
Embedding Transfer with Label Relaxation for Improved Metric Learning
Sungyeon Kim, Dongwon Kim, Minsu Cho +1
This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities betwe…
Proxy Anchor Loss for Deep Metric Learning
Sungyeon Kim, Dongwon Kim, Minsu Cho +1
Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data p…
Deep Metric Learning Beyond Binary Supervision
Sungyeon Kim, Minkyo Seo, Ivan Laptev +2
Metric Learning for visual similarity has mostly adopted binary supervision indicating whether a pair of images are of the same class or not. Such a binary indicator covers only a…