36 citations · 90 across the 21 of their papers we have counts for
7 papers · 1 filter
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder
Seunghyuk Cho, Zhenyue Qin, Yang Liu +3
We introduce GeoDANO, a geometric vision-language model (VLM) with a domain-agnostic vision encoder, for solving plane geometry problems. Although VLMs have been employed for solvi…
Feature Unlearning for Pre-trained GANs and VAEs
Saemi Moon, Seunghyuk Cho, Dongwoo Kim
We tackle the problem of feature unlearning from a pre-trained image generative model: GANs and VAEs. Unlike a common unlearning task where an unlearning target is a subset of the…
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Zhenyue Qin, Pan Ji, Dongwoo Kim +3
Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate compo…
Representation-Centric Survey of Supervised Skeletal Action Recognition and the New Benchmark
Yang Liu, Jiyao Yang, Madhawa Perera +8
3D skeletal action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches, offering robustness to environmental variations, computational e…
Anonymization for Skeleton Action Recognition
Saemi Moon, Myeonghyeon Kim, Zhenyue Qin +2
Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skele…
Informative Class Activation Maps
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theo…
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Zhenyue Qin, Yang Liu, Pan Ji +5
Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…