Multi-Modal14 [2025-1] 정인아 - CoCa: Contrastive Captioners are Image-Text Foundation Models https://arxiv.org/abs/2205.01917 CoCa: Contrastive Captioners are Image-Text Foundation ModelsExploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain anarxiv.org Intro문제Captioning과 Contrastive Learnin.. 2025. 1. 25. [2024-2] 박서형 - DETR , Deformable DETR https://arxiv.org/abs/2005.12872 End-to-End Object Detection with TransformersWe present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor genearxiv.org DETR은 transformer를 이용하여 post processing 없이 object dete.. 2024. 12. 28. [2024-1] 백승우 - VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer (VATT) takes raw signals as inputs and extracts multimodal representations t arxiv.org 1. Abstract VATT는 트랜스포머 아키텍처를 사용해, 레이블이 없.. 2024. 3. 4. [2023-2] 백승우 - 🦩 Flamingo: a Visual Language Model for Few-Shot Learning Flamingo: a Visual Language Model for Few-Shot Learning Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propo arxiv.org 0. Abstract Flamingo의 주요 아키텍쳐 발전 (1) 사전 학습된 강력한 시각 전용 모델과 언어 전용 모델을 연결 (2) .. 2024. 2. 23. 이전 1 2 3 4 다음
