5 citations · 7 across the 5 of their papers we have counts for
5 papers
See It All: Contextualized Late Aggregation for 3D Dense Captioning
Minjung Kim, Hyung Suk Lim, Seung Hwan Kim +3
3D dense captioning is a task to localize objects in a 3D scene and generate descriptive sentences for each object. Recent approaches in 3D dense captioning have adopted transforme…
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining
Bumsoo Kim, Yeonsik Jo, Jinhyung Kim +1
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web. To enhance data-effic…
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
Jeeho Hyun, Sangyun Kim, Giyoung Jeon +3
Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the r…
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
Taehoon Kim, Mark Marsden, Pyunghwan Ahn +4
When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To…
L-Verse: Bidirectional Generation Between Image and Text
Taehoon Kim, Gwangmo Song, Sihaeng Lee +6
Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially…