51 citations · 71 across the 6 of their papers we have counts for
6 papers
Unfolding Videos Dynamics via Taylor Expansion
Siyi Chen, Minkyu Choi, Zesen Zhao +3
Taking inspiration from physical motion, we present a new self-supervised dynamics learning strategy for videos: Video Time-Differentiation for Instance Discrimination (ViDiDi). Vi…
A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
Minkyu Choi, Kuan Han, Xiaokai Wang +2
The human visual system uses two parallel pathways for spatial processing and object recognition. In contrast, computer vision systems tend to use a single feedforward pathway, ren…
Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises
Minkyu Choi, Yizhen Zhang, Kuan Han +2
Humans actively observe the visual surroundings by focusing on salient objects and ignoring trivial details. However, computer vision models based on convolutional neural networks…
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning
Yizhen Zhang, Minkyu Choi, Kuan Han +1
In natural language processing, most models try to learn semantic representations merely from texts. The learned representations encode the distributional semantics but fail to con…
Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition
Kuan Han, Haiguang Wen, Yizhen Zhang +3
Inspired by "predictive coding" - a theory in neuroscience, we develop a bi-directional and dynamic neural network with local recurrent processing, namely predictive coding network…
Deep Predictive Coding Network for Object Recognition
Haiguang Wen, Kuan Han, Junxing Shi +3
Based on the predictive coding theory in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN). It has feedforward, feed…