5 papers
Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning
Chinthani Sugandhika, Chen Li, Deepu Rajan +1
Large Video-Language Models (Video-LMs) have achieved impressive progress in multimodal understanding, yet their reasoning remains weakly grounded in space and time. We present Kno…
VOST-SGG: VLM-Aided One-Stage Spatio-Temporal Scene Graph Generation
Chinthani Sugandhika, Chen Li, Deepu Rajan +1
Spatio-temporal scene graph generation (ST-SGG) aims to model objects and their evolving relationships across video frames, enabling interpretable representations for downstream re…
Next-Frame Feature Prediction for Multimodal Deepfake Detection and Temporal Localization
Ashutosh Anshul, Shreyas Gopal, Deepu Rajan +1
Recent multimodal deepfake detection methods designed for generalization conjecture that single-stage supervised training struggles to generalize across unseen manipulations and da…
IMoRe: Implicit Program-Guided Reasoning for Human Motion Q&A
Chen Li, Chinthani Sugandhika, Yeo Keat Ee +5
Existing human motion Q\&A methods rely on explicit program execution, where the requirement for manually defined functional modules may limit the scalability and adaptability. To…
Situational Scene Graph for Structured Human-centric Situation Understanding
Chinthani Sugandhika, Chen Li, Deepu Rajan +1
Graph based representation has been widely used in modelling spatio-temporal relationships in video understanding. Although effective, existing graph-based approaches focus on capt…