115 citations · 191 across the 29 of their papers we have counts for
30 papers
Early Action Recognition with Action Prototypes
Guglielmo Camporese, Alessandro Bergamo, Xunyu Lin +2
Early action recognition is an important and challenging problem that enables the recognition of an action from a partially observed video stream where the activity is potentially…
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
Haodong Duan, Mingze Xu, Bing Shuai +4
We present SkeleTR, a new framework for skeleton-based action recognition. In contrast to prior work, which focuses mainly on controlled environments, we target more general scenar…
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Qin Zhang, Linghan Xu, Qingming Tang +4
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes an…
ScaleDet: A Scalable Multi-Dataset Object Detector
Yanbei Chen, Manchen Wang, Abhay Mittal +4
Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset…
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
Zhenlin Xu, Yi Zhu, Tiffany Deng +6
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks li…
Learning for Transductive Threshold Calibration in Open-World Recognition
Qin Zhang, Dongsheng An, Tianjun Xiao +6
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true n…
Large Scale Real-World Multi-Person Tracking
Bing Shuai, Alessandro Bergamo, Uta Buechler +3
This paper presents a new large scale multi-person tracking dataset -- \texttt{PersonPath22}, which is over an order of magnitude larger than currently available high quality multi…
An In-depth Study of Stochastic Backpropagation
Jun Fang, Mingze Xu, Hao Chen +3
In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP) when training deep neural networks for standard image classification and object detection tasks. Dur…
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions
A S M Iftekhar, Hao Chen, Kaustav Kundu +3
We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize huma…
SCVRL: Shuffled Contrastive Video Representation Learning
Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli +2
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learni…
Hierarchical Self-supervised Representation Learning for Movie Understanding
Fanyi Xiao, Kaustav Kundu, Joseph Tighe +1
Most self-supervised video representation learning approaches focus on action recognition. In contrast, in this paper we focus on self-supervised video learning for movie understan…
Transfer of Representations to Video Label Propagation: Implementation Factors Matter
Daniel McKee, Zitong Zhan, Bing Shuai +3
This work studies feature representations for dense label propagation in video, with a focus on recently proposed methods that learn video correspondence using self-supervised sign…
Multi-Object Tracking with Hallucinated and Unlabeled Videos
Daniel McKee, Bing Shuai, Andrew Berneshawi +4
In this paper, we explore learning end-to-end deep neural trackers without tracking annotations. This is important as large-scale training data is essential for training deep neura…
Video Contrastive Learning with Global Context
Haofei Kuang, Yi Zhu, Zhi Zhang +5
Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastiv…
Single View Physical Distance Estimation using Human Pose
Xiaohan Fei, Henry Wang, Xiangyu Zeng +3
We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video ca…
MaCLR: Motion-aware Contrastive Learning of Representations for Videos
Fanyi Xiao, Joseph Tighe, Davide Modolo
We present MaCLR, a novel method to explicitly perform cross-modal self-supervised video representations learning from visual and motion modalities. Compared to previous video repr…
SiamMOT: Siamese Multi-Object Tracking
Bing Shuai, Andrew Berneshawi, Xinyu Li +2
In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. Si…
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
Zhe Wang, Hao Chen, Xinyu Li +4
Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos…
VidTr: Video Transformer Without Convolutions
Yanyi Zhang, Xinyu Li, Chunhui Liu +6
We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal infor…
TubeR: Tubelet Transformer for Video Action Detection
Jiaojiao Zhao, Yanyi Zhang, Xinyu Li +10
We propose TubeR: a simple solution for spatio-temporal video action detection. Different from existing methods that depend on either an off-line actor detector or hand-designed ac…
Selective Feature Compression for Efficient Activity Recognition Inference
Chunhui Liu, Xinyu Li, Hao Chen +2
Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world appli…
NUTA: Non-uniform Temporal Aggregation for Action Recognition
Xinyu Li, Chunhui Liu, Bing Shuai +3
In the world of action recognition research, one primary focus has been on how to construct and train networks to model the spatial-temporal volume of an input video. These methods…
A Comprehensive Study of Deep Video Action Recognition
Yi Zhu, Xinyu Li, Chunhui Liu +7
Video action recognition is one of the representative tasks for video understanding. Over the last decade, we have witnessed great advancements in video action recognition thanks t…
Directional Temporal Modeling for Action Recognition
Xinyu Li, Bing Shuai, Joseph Tighe
Many current activity recognition models use 3D convolutional neural networks (e.g. I3D, I3D-NL) to generate local spatial-temporal features. However, such features do not encode c…