목록전체 글 (122)

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[논문 리뷰] VadCLIP: Adapting Vision-Language Models for Weakly SupervisedVideo Anomaly Detection (2023)

이번에 리뷰할 논문은 VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection 입니다. https://arxiv.org/abs/2308.11681 VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly DetectionThe recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerfu..
논문 리뷰/Anomaly Detection 2025. 4. 4. 23:43
[논문 리뷰] VQ-GAN: Taming Transformers for High-Resolution Image Synthesis (2021)

이번에 리뷰할 논문은 Taming Transformers for High-Resolution Image Synthesis 입니다.https://arxiv.org/abs/2012.09841 Taming Transformers for High-Resolution Image SynthesisDesigned to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This..
논문 리뷰/Image generation 2025. 3. 28. 15:10
[논문 리뷰] VQ-VAE: Neural Discrete Representation Learning (2018)

이번에 리뷰할 논문은 Neural Discrete Representation Learning 입니다.https://arxiv.org/abs/1711.00937 Neural Discrete Representation LearningLearning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncarxiv.org     ..
논문 리뷰/Image generation 2025. 3. 24. 17:51
[논문 리뷰] ANOLE: AnOpen,Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation (2024)

이번에 리뷰할 논문은 ANOLE: AnOpen,Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation 입니다. [2407.06135] ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text GenerationPrevious open-source large multimodal models (LMMs) have faced sever..
논문 리뷰/Multi-Modal 2025. 3. 20. 11:50
[논문 리뷰] Chameleon: Mixed-Modal Early-Fusion FoundationModels (2024)

이번에 리뷰할 논문은 Chameleon: Mixed-Modal Early-Fusion FoundationModels 입니다. https://arxiv.org/abs/2405.09818 Chameleon: Mixed-Modal Early-Fusion Foundation ModelsWe present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an..
논문 리뷰/Multi-Modal 2025. 3. 18. 16:02
[논문 리뷰] Imagine while Reasoning in Space:Multimodal Visualization-of-Thought (2025)

이번에 리뷰할 논문은 Imagine while Reasoning in Space: Multimodal Visualization-of-Thought 입니다. https://arxiv.org/abs/2501.07542 Imagine while Reasoning in Space: Multimodal Visualization-of-ThoughtChain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). Yet, it struggles in complex spatial r..
논문 리뷰/Multi-Modal 2025. 3. 14. 15:46
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