6 citations · 13 across the 7 of their papers we have counts for
9 papers
PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation
Chunghyun Park, Beomjun Kim, Seungcheol Park +5
World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches ty…
Exploring High-Order Self-Similarity for Video Understanding
Manjin Kim, Heeseung Kwon, Karteek Alahari +1
Space-time self-similarity (STSS), which captures visual correspondences across frames, provides an effective way to represent temporal dynamics for video understanding. In this wo…
ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
Huiwon Jang, Sihyun Yu, Heeseung Kwon +3
Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains wh…
Lightweight Structure-Aware Attention for Visual Understanding
Heeseung Kwon, Francisco M. Castro, Manuel J. Marin-Jimenez +2
Attention operator has been widely used as a basic brick in visual understanding since it provides some flexibility through its adjustable kernels. However, this operator suffers f…
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…