9 citations · 31 across the 19 of their papers we have counts for
20 papers
THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage
Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren +3
The rapid proliferation of video in applications such as autonomous driving, surveillance, and sports analytics necessitates robust methods for dynamic scene understanding. Despite…
InsTALL: Context-aware Instructional Task Assistance with Multi-modal Large Language Models
Pha Nguyen, Sailik Sengupta, Girik Malik +2
The improved competence of generative models can help building multi-modal virtual assistants that leverage modalities beyond language. By observing humans performing multi-step ta…
HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation
Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren +2
Multimodal LLMs have advanced vision-language tasks but still struggle with understanding video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to cap…
DINTR: Tracking via Diffusion-based Interpolation
Pha Nguyen, Ngan Le, Jackson Cothren +2
Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames. Diffusion models have shown remarkable capabilities…
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
Trong-Thuan Nguyen, Pha Nguyen, Xin Li +3
Video scene graph generation (VidSGG) has emerged as a transformative approach to capturing and interpreting the intricate relationships among objects and their temporal dynamics i…
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding
Trong-Thuan Nguyen, Pha Nguyen, Khoa Luu
Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a si…
HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos
Naga VS Raviteja Chappa, Pha Nguyen, Thi Hoang Ngan Le +1
Group Activity Scene Graph (GASG) generation is a challenging task in computer vision, aiming to anticipate and describe relationships between subjects and objects in video sequenc…
REACT: Recognize Every Action Everywhere All At Once
Naga VS Raviteja Chappa, Pha Nguyen, Page Daniel Dobbs +1
Group Activity Recognition (GAR) is a fundamental problem in computer vision, with diverse applications in sports video analysis, video surveillance, and social scene understanding…
Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes
Fabien Delattre, David Dirnfeld, Phat Nguyen +4
We present an approach to estimating camera rotation in crowded, real-world scenes from handheld monocular video. While camera rotation estimation is a well-studied problem, no pre…
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation
Pha Nguyen, Kha Gia Quach, John Gauch +3
Multiple Object Tracking (MOT) aims to find bounding boxes and identities of targeted objects in consecutive video frames. While fully-supervised MOT methods have achieved high acc…
Z-GMOT: Zero-shot Generic Multiple Object Tracking
Kim Hoang Tran, Anh Duy Le Dinh, Tien Phat Nguyen +6
Despite recent significant progress, Multi-Object Tracking (MOT) faces limitations such as reliance on prior knowledge and predefined categories and struggles with unseen objects.…
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
Pha Nguyen, Kha Gia Quach, Kris Kitani +1
One of the recent trends in vision problems is to use natural language captions to describe the objects of interest. This approach can overcome some limitations of traditional meth…
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
Naga VS Raviteja Chappa, Pha Nguyen, Alexander H Nelson +4
This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To…
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
Naga VS Raviteja Chappa, Pha Nguyen, Alexander H Nelson +4
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 dat…
Contextual Explainable Video Representation: Human Perception-based Understanding
Khoa Vo, Kashu Yamazaki, Phong X. Nguyen +3
Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action…
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
Pha Nguyen, Kha Gia Quach, Chi Nhan Duong +3
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. It is e…
Depth Perspective-aware Multiple Object Tracking
Kha Gia Quach, Huu Le, Pha Nguyen +3
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. Indee…
Self-supervised Domain Adaptation in Crowd Counting
Pha Nguyen, Thanh-Dat Truong, Miaoqing Huang +3
Self-training crowd counting has not been attentively explored though it is one of the important challenges in computer vision. In practice, the fully supervised methods usually re…
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
Pha Nguyen, Kha Gia Quach, Chi Nhan Duong +3
The development of autonomous vehicles provides an opportunity to have a complete set of camera sensors capturing the environment around the car. Thus, it is important for object d…
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Kha Gia Quach, Pha Nguyen, Huu Le +4
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number…