목록논문 리뷰 (44)

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[논문 리뷰] Mind with Eyes: from Language Reasoning toMultimodal Reasoning (2025)

이번에 리뷰할 논문은 Mind with Eyes: from Language Reasoning to Multimodal Reasoning 입니다.https://arxiv.org/abs/2503.18071 Mind with Eyes: from Language Reasoning to Multimodal ReasoningLanguage models have recently advanced into the realm of reasoning, yet it is through multimodal reasoning that we can fully unlock the potential to achieve more comprehensive, human-like cognitive capabilities. This surve..
논문 리뷰/Multi-Modal 2025. 4. 10. 18:33
[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
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