🐍 (한국어) Reasoning Mamba: Hypergraph + Mamba로 Affordance Grounding 문제 해결! 제목: Reasoning Mamba: Hypergraph-Guided Region Relation Calculating for Weakly Supervised Affordance Grounding 학회: CV...
🎭 (English) MaskPrompt: Achieving Open-Vocabulary Affordance Segmentation with Object Shape Mask Prompts! Title: MaskPrompt: Open-Vocabulary Affordance Segmentation with Object Shape Mask Pro...
🖼️ (한국어) Qwen2.5-VL: 다이나믹 해상도와 초장기 비디오 이해까지! 제목: Qwen2.5-VL Technical Report 학회: arXiv (2025년 2월, Alibaba Qwen Team) 코드/체크포인트: GitHub – Qwen2.5-VL 핵심 키워드: Vision-Language Model, Dynamic...
🎥 LAVAD: Training-free Video Anomaly Detection with LLM! LA-VAD = LAnguage-based Video Anomaly Detection. In other words, language model-based video anomaly detection!! Title: Harnessing ...
📍 GEM: Unlocking the Latent Localization Ability of VLMs! Title: Grounding Everything: Emerging Localization Properties in Vision-Language Transformers Conference: CVPR 2024 Code/Checkpoi...
개요 이 포스트에서는 VL-SAM의 핵심인 “객체 이름 → 위치 힌트(Attention Map) 생성 → SAM 포인트 프롬프트” 중, Attention Map 생성(VLM 측) 실습을 다룹니다. (반복 iteration 없이, 단일 패스 성격의 데모) 1) [Object Recognition] – Attention Map Generation ...
🔎 VL-SAM: Training-Free Open-Ended Object Detection & Segmentation Title: Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts Conference: NeurIPS 2024 ...
🔎 (English) CLIP Surgery: Enhancing Explainability by Operating on CLIP! Title: A Closer Look at the Explainability of Contrastive Language-Image Pre-training (CLIP Surgery) Journal: Patter...
🧩 (한국어) PartCLIPSeg: CLIP으로 파트 단위까지 인식하는 Open-Vocabulary 세분화! 제목: PartCLIPSeg: Understanding Multi-Granularity for Open-Vocabulary Part Segmentation 학회: NeurIPS 2024 코드/체크포인트: GitHub – Pa...
🧬 (English) Practicing ClipSurgery!! Today we will practice with the CLIP model that has been surgically modified — ClipSurgery! We’ll run it directly and generate a clean similarity map (CAM) on...