6 citations · 11 across the 2 of their papers we have counts for
5 papers
Relational Self-Attention: What's Missing in Attention for Video Understanding
Manjin Kim, Heeseung Kwon, Chunyu Wang +2
Convolution has been arguably the most important feature transform for modern neural networks, leading to the advance of deep learning. Recent emergence of Transformer networks, wh…
Relational Embedding for Few-Shot Classification
Dahyun Kang, Heeseung Kwon, Juhong Min +1
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational p…
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition
Heeseung Kwon, Manjin Kim, Suha Kwak +1
Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this pape…
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
Heeseung Kwon, Manjin Kim, Suha Kwak +1
Motion plays a crucial role in understanding videos and most state-of-the-art neural models for video classification incorporate motion information typically using optical flows ex…
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
Gyeongsik Moon, Heeseung Kwon, Kyoung Mu Lee +1
Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting conte…