7 papers
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition
Di Yang, Mahmoud Ali, Quan Kong +2
Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing models rely primarily on appeara…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living
Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha +5
Current Large Language Vision Models (LLVMs) trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interact…
SKI Models: Skeleton Induced Vision-Language Embeddings for Understanding Activities of Daily Living
Arkaprava Sinha, Dominick Reilly, Francois Bremond +2
The introduction of vision-language models like CLIP has enabled the development of foundational video models capable of generalizing to unseen videos and human actions. However, t…
Loose Social-Interaction Recognition in Real-world Therapy Scenarios
Abid Ali, Rui Dai, Ashish Marisetty +5
The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying-object, etc. However, with the advancement in deep learning models, ther…
Introducing Gating and Context into Temporal Action Detection
Aglind Reka, Diana Laura Borza, Dominick Reilly +2
Temporal Action Detection (TAD), the task of localizing and classifying actions in untrimmed video, remains challenging due to action overlaps and variable action durations. Recent…