Search Results for author:
Found 5 papers, 5 papers with code
CREMA: Multimodal Compositional Video Reasoning via Efficient Modular Adaptation and Fusion
Furthermore, we propose a fusion module designed to compress multimodal queries, maintaining computational efficiency in the LLM while combining additional modalities.
Ranked #1 on
Question Answering
on SQA3D
A Simple LLM Framework for Long-Range Video Question-Answering
Furthermore, we show that a specialized prompt that asks the LLM first to summarize the noisy short-term visual captions and then answer a given input question leads to a significant LVQA performance boost.
Ranked #1 on
Zero-Shot Video Question Answer
on NExT-GQA
Self-Chained Image-Language Model for Video Localization and Question Answering
SeViLA framework consists of two modules: Localizer and Answerer, where both are parameter-efficiently fine-tuned from BLIP-2.
Ranked #3 on
Zero-Shot Video Question Answer
on IntentQA
(using extra training data)
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise.
Anomaly Detection In Surveillance Videos
Optical Flow Estimation
+1
STAR: A Benchmark for Situated Reasoning in Real-World Videos
This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR).
