251 citations · 442 across the 40 of their papers we have counts for
13 papers · 1 filter
Dynamic Temporal Filtering in Video Models
Fuchen Long, Zhaofan Qiu, Yingwei Pan +3
Video temporal dynamics is conventionally modeled with 3D spatial-temporal kernel or its factorized version comprised of 2D spatial kernel and 1D temporal kernel. The modeling powe…
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
Zhaofan Qiu, Yehao Li, Yu Wang +3
In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded ``Selective Position Encoding (SPE)'' procedure relies…
Explaining Cross-Domain Recognition with Interpretable Deep Classifier
Yiheng Zhang, Ting Yao, Zhaofan Qiu +1
The recent advances in deep learning predominantly construct models in their internal representations, and it is opaque to explain the rationale behind and decisions to human users…
Lightweight and Progressively-Scalable Networks for Semantic Segmentation
Yiheng Zhang, Ting Yao, Zhaofan Qiu +1
Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-worl…
Bi-Calibration Networks for Weakly-Supervised Video Representation Learning
Fuchen Long, Ting Yao, Zhaofan Qiu +3
The leverage of large volumes of web videos paired with the searched queries or surrounding texts (e.g., title) offers an economic and extensible alternative to supervised video re…
Stand-Alone Inter-Frame Attention in Video Models
Fuchen Long, Zhaofan Qiu, Yingwei Pan +3
Motion, as the uniqueness of a video, has been critical to the development of video understanding models. Modern deep learning models leverage motion by either executing spatio-tem…
MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing
Zhaofan Qiu, Ting Yao, Chong-Wah Ngo +1
Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MS…
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
Yingwei Pan, Yehao Li, Yiheng Zhang +5
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Inter…
Motion-Focused Contrastive Learning of Video Representations
Rui Li, Yiheng Zhang, Zhaofan Qiu +3
Motion, as the most distinct phenomenon in a video to involve the changes over time, has been unique and critical to the development of video representation learning. In this paper…
Representing Videos as Discriminative Sub-graphs for Action Recognition
Dong Li, Zhaofan Qiu, Yingwei Pan +3
Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore…
Boosting Video Representation Learning with Multi-Faceted Integration
Zhaofan Qiu, Ting Yao, Chong-Wah Ngo +3
Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in th…
Condensing a Sequence to One Informative Frame for Video Recognition
Zhaofan Qiu, Ting Yao, Yan Shu +2
Video is complex due to large variations in motion and rich content in fine-grained visual details. Abstracting useful information from such information-intensive media requires ex…
Optimization Planning for 3D ConvNets
Zhaofan Qiu, Ting Yao, Chong-Wah Ngo +1
It is not trivial to optimally learn a 3D Convolutional Neural Networks (3D ConvNets) due to high complexity and various options of the training scheme. The most common hand-tuning…