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Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program
Tiange Luo, Honglak Lee, Justin Johnson
3D shapes have complementary abstractions from low-level geometry to part-based hierarchies to languages, which convey different levels of information. This paper presents a unifie…
Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders
Jongseong Jang, Daeun Kyung, Seung Hwan Kim +3
Deep neural networks are increasingly used in medical imaging for tasks such as pathological classification, but they face challenges due to the scarcity of high-quality, expert-la…
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…
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim +3
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under…
RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects
Yunseok Jang, Ruben Villegas, Jimei Yang +3
There have been remarkable successes in computer vision with deep learning. While such breakthroughs show robust performance, there have still been many challenges in learning in-d…