Search Results for author:
Found 13 papers, 2 papers with code
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding
In this paper, we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects.
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos
Flow-Attention incorporates flow conservation principles, fostering competition for sources and allocation for sinks, effectively preventing the generation of trivial attention.
REACT: Recognize Every Action Everywhere All At Once
Group Activity Recognition (GAR) is a fundamental problem in computer vision, with diverse applications in sports video analysis, video surveillance, and social scene understanding.
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation
Then, a new cross-domain MOT adaptation from existing datasets is proposed without any pre-defined human knowledge in understanding and modeling objects.
Z-GMOT: Zero-shot Generic Multiple Object Tracking
Our contributions are benchmarked through extensive experiments conducted on the Referring GMOT dataset for GMOT task.
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objects in videos by typing natural language descriptions.
Grounded Multiple Object Tracking
Multiple Object Tracking
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SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data.
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data.
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car.
Depth Perspective-aware Multiple Object Tracking
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions.
Self-supervised Domain Adaptation in Crowd Counting
Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision.
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
