목록전체 글 (145)

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[논문 리뷰] VAU-R1: Advancing Video Anomaly Understandingvia Reinforcement Fine-Tuning(2025)

이번에 리뷰할 논문은 VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning 입니다.https://arxiv.org/abs/2505.23504 VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-TuningVideo Anomaly Understanding (VAU) is essential for applications such as smart cities, security surveillance, and disaster alert systems, yet remains challenging due to its demand for fine-grained sp..
논문 리뷰/Anomaly Detection 2025. 9. 22. 20:05
[논문 리뷰] VERA: Explainable Video Anomaly Detection via Verbalized Learning ofVision-Language Models(2025)

이번에 리뷰할 논문은 VERA: Explainable Video Anomaly Detection via Verbalized Learning ofVision-Language Models 입니다.https://arxiv.org/abs/2412.01095 VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language ModelsThe rapid advancement of vision-language models (VLMs) has established a new paradigm in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomal..
논문 리뷰/Anomaly Detection 2025. 9. 20. 19:38
[논문 리뷰] Follow the Rules: Reasoning for Video AnomalyDetection with Large Language Models(2024)

이번에 리뷰할 논문은 Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models 입니다.https://arxiv.org/abs/2407.10299 Follow the Rules: Reasoning for Video Anomaly Detection with Large Language ModelsVideo Anomaly Detection (VAD) is crucial for applications such as security surveillance and autonomous driving. However, existing VAD methods provide little rationale behind detection,..
논문 리뷰/Anomaly Detection 2025. 8. 20. 15:41
[논문 리뷰] TIPS: TEXT-IMAGE PRETRAINING WITH SPATIALAWARENESS(2025)

이번에 리뷰할 논문은 TIPS: TEXT-IMAGE PRETRAINING WITH SPATIAL AWARENESS 입니다. https://arxiv.org/abs/2410.16512 TIPS: Text-Image Pretraining with Spatial awarenessWhile image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense understanding tasks. For this reason, self-supervised image-only pre..
논문 리뷰/Vision-Language model 2025. 8. 13. 17:31
[논문 리뷰] SILC: Improving Vision Language Pretraining with Self-Distillation(2023)

이번에 리뷰할 논문은 SILC: Improving Vision Language Pretraining with Self-Distillation 입니다.https://arxiv.org/abs/2310.13355 SILC: Improving Vision Language Pretraining with Self-DistillationImage-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also..
논문 리뷰/Vision-Language model 2025. 7. 31. 16:28
[논문 리뷰] LocCa: Visual Pretraining with Location-aware Captioners(2024)

이번에 리뷰할 논문은 LocCa: Visual Pretraining with Location-aware Captioners 입니다.https://arxiv.org/abs/2403.19596 LocCa: Visual Pretraining with Location-aware CaptionersImage captioning has been shown as an effective pretraining method similar to contrastive pretraining. However, the incorporation of location-aware information into visual pretraining remains an area with limited research. In this paper..
논문 리뷰/Vision-Language model 2025. 7. 30. 15:18
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