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- 🐍 Reasoning Mamba: Hypergraph 기반 추론으로 Weakly Supervised Affordance Grounding 강화!
- 🎭 MaskPrompt: 오픈 보캐뷸러리 Affordance Segmentation을 위한 객체 마스크 프롬프트
- 🖼️ Qwen2.5-VL: Next-Gen Vision-Language Model with Dynamic Resolution & Long Video Understanding
- 🎥 LAVAD: Training-Free Video Anomaly Detection with LLMs
- 📍 GEM: Grounding Everything in Vision-Language Transformers
- 🔎 VL-SAM Hands-on: VL-SAM 을 실습해보자!
- 🔎 VL-SAM: Training-Free Open-Ended Object Detection & Segmentation
- 🔎 CLIP Surgery: A Closer Look at the Explainability of Contrastive Language-Image Pre-training
- 🧩 PartCLIPSeg: Open-Vocabulary Part-level Segmentation with CLIP Guidance
- 🔎 ClipSurgery Hands-on: ClipSurgery 을 실습해보자!
- 🧠 SAM2 Hands-On Practice!! : SAM2 실습!! with Python
- 🔎 Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
- 🧩 Segment Anything, Even Occluded (SAMEO): 가려진 부분까지 세그멘트하는 SAM 확장
- 🧠 EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything — 실전형 SAM의 표준
- 🧩 RTMDet, SOTA of Real-Time, One-Stage Object Detectors: 실시간, One-Stage Object Detector의 정수
- 🎨 An Image is Worth One Word: Textual Inversion - 이미지를 `거시기` 화 해보리기!!
- 📝 TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
- 📊 Evaluation Metrics in CIRR - CIRR분야의 Metrics 알아보기
- 🧠 OSrCIR: Reason-before-Retrieve for Composed Image Retrieval
- 🧠 [CIReVL] VISION-BY-LANGUAGE FOR TRAINING-FREE COMPOSITIONAL IMAGE RETRIEVAL : First Training Free on CIRR
- 🧠 CIRCO - Zero-Shot Composed Image Retrieval with Textual Inversion (ICCV 2023)
- 🧠 CIR - Composed Image Retrieval on Real-life Images : 이미지 탐색의 시작연구!!
- 🧠 FashionIQ - Fashion Image Retrieval with Natural Language: 패션 이미지 검색의 새로운 표준
- 👁️ MLLMs Know Where to Look: Training-free Visual Detail Perception
- 🧠 Notes-guided MLLM Reasoning
- 🚀Understanding Wikidata -무료료 wiki 데이터 활용하기!!
- 🧠Lost in the Middle - 긴 문맥에서 언어모델이 진짜 정보를 기억할까?
- 🔍 WSAG-PLSP: Weakly Supervised 학습을 통한 Affordance Grounding 문제해결!
- 🔍 WSMA: Multimodal Weak Supervision으로 Egocentric Affordance Grounding 혁신!
- 📌 LOCATE: Weakly Supervised Affordance Grounding을 위한 Object Part Localization & Transfer
- [MLflow] LLM 프롬프트 엔지니어링 실험 관리하기 - 체계적인 프롬프트 튜닝과 결과 추적
- 🚀Understanding MLflow -MLOps의 필수 도구 MLflow 알아보기?!!
- 🚀 Transformer 파이썬으로 이해하기!
- 🔤Understanding Tokenizers - Tokenizer 알아보기?!!
- 🔗 Understanding GLIP - CLIP이해하기!!!
- 📝Understanding YOLO-World - 실시간 Open-Vocabulary Object Detection의 혁신!!!
- 📝Understanding CLIP4HOI - CLIP4HOI를 알아보자!!!
- 📝Understanding EZ-HOI - EZ-HOI 알아보기!!
- 📝Understanding CLIP-Adapter - CLIP-Adapter 알아보기?!!
- 📝Understanding YOLO - YOLO 알아보기?!!
- 🖥️ LoRA Hands-On Practice!! : LORA 실습!! with python
- 📝Understanding BLIP - BLIP 알아보기?!!
- 📝Understanding FG-CLip - FG-Clip 알아보기?!!
- 🖥️ FG-Clip Practice!! : FG-Clip 실습!! with python
- 📝 LoRA: Low-Rank Fine-Tuning for Large Language Models - Understanding LORA- LORA 알아보기?!!
- 🖥️ SEEM Practice!! - SEEM 실습!! with python. gradio
- 📝 Understanding SEEM - SEEM(Segment Everything Everywhere All at Once) 알아보기!!
- 🖥️ Grounding DINO 1.5 Practice!! - Grounding DINO 1.5 실습!!
- 📝 Understanding LISA - LISA 알아보기?!!
- 🖥️ LISA Practice!! - Reasoning Segmentation LLM LISA 실습!!
- 🖥️ Video segmentation with Python using SAM2! - 파이썬 SAM2 실습 : 비디오에서 누끼따기!
- 🧠 Understanding SAM2 - SAM2 알아보기?!!
- 📝 Understanding Grounding DINO!! - Grounding DINO 논문 공부!
- AI에서 'Ground'란 무엇인가? Grounding DINO, Grounding SAM, 그리고 Grounded Affordance까지!
- 🖥️ Grounded SAM Hands-On with Python! - Grounded SAM 실습 with python!
- 🖥️ Grounding DINO Practice - Grounding DINO 실습 with python!
- 🖥️ DINO Practice: Running Object Detection with Pretrained Models - DINO 실습: 모델을 받아 직접 객체 탐지 해보기!
- 📝 DINO: The Evolutionary Object Detection Model of DETR!! - DINO: DETR의 진화형 객체 탐지 모델!! (ICLR 2023)
- 🖥️ Object Detection with DETR! Python Practice!! - DETR을 활용한 객체 탐지! 파이썬 실습!!
- The Rise and Fall of UAE's Once-Prominent LLM, Falcon - 한때 주목받던 UAE의 LLM, 팰컨의 근황
- 📝 The First Transformer-based Image Detection Model!! DETR! - Transformer로 객채 탐지까지!! DETR의 등장!! (CVPR 2020)
- 🖥️ DINO Python Experiment!! Super Impressive!! - DINO 파이썬 실습!! 완전 신기해!!
- 🖥️ Image segmentation with Python using SAM! - 파이썬으로 누끼따기!? SAM (Segment Anything Model) 실습
- 📝 Segment Anything, You are amazing! - 누끼의 괴물, SAM의 등장!! (ICCV, 2023)
- 📝 ViT, you can do greater things! - The emergence of DINO!! // ViT, 너는 더 큰일을 할수있어! - DINO의 등장!! (ICCV 2021)
- The Relentless Rise of Chinese AI Models!! A Look at Qwen3!! - 끝없는 중국AI모델의 발전!! Qwen3 살펴보기!! 🇨🇳🚀
- 🖥️ Image classification using ViT with Python - 파이썬으로 ViT 모델을 활용, 이미지 분류하기
- 🖥️ Studying CAM with Python! - 파이썬으로 CAM 공부하기
- 📝 Peeking into the Mind of AI: Understanding CAM! - AI의 속마음을 들여다본다!! CAM 알아보기
- Using CLIP with Python - 파이썬으로 CLIP을 사용해보기
- On April 5th, 2025, Meta unveiled their next-gen multimodal AI model — Llama 4! 🦙🚀 - Meta에서 Llama 4 모델 공개!
- 📝 Understanding CLIP - CLIP 모델 이해하기
- Exploring Major Journals in AI - AI와 관련된 주요 저널 알아보기 (feat. h-index)
- Newly upgraded Sora, now writes well too. - 새로 업그레이드된 소라, 글씨도 잘 써요
- 📝 Image? You Can Do Transformer Too!! - The Emergence of ViT!! - 이미지? 너도 Transformer 할수있어!! - ViT의 등장!! (ICLR 2021)