---
# System prepended metadata

title: Segmentation
tags: [IMAM]

---

# Segmentation

## Segmentation Model (Non Specific)
### Diffusion 계열
- DiffSeg [CVPR2024]: https://arxiv.org/pdf/2308.12469
- OVAM [CVPR2024] (찬영 친구가 쓴 논문): https://openaccess.thecvf.com/content/CVPR2024/papers/Marcos-Manchon_Open-Vocabulary_Attention_Maps_with_Token_Optimization_for_Semantic_Segmentation_in_CVPR_2024_paper.pdf
- ConceptAttention [ICML2025]
- Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers

### CLIP 계열
- CLIPSeg 
- ZegCLIP: https://arxiv.org/pdf/2212.03588
- ViCLIP: 

### DINO (Self-supervised DINO)
- TokenCut [CVPR2022]: https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Self-Supervised_Transformers_for_Unsupervised_Object_Discovery_Using_Normalized_Cut_CVPR_2022_paper.pdf
- :video_camera: SSL-VOS [WACV2023]: https://openaccess.thecvf.com/content/WACV2023/papers/Ponimatkin_A_Simple_and_Powerful_Global_Optimization_for_Unsupervised_Video_Object_WACV_2023_paper.pdf
- :video_camera: Betrayed by Attention [ECCV2024]: https://arxiv.org/pdf/2311.17893
- :video_camera: VideoCutLER [CVPR2024]: https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_VideoCutLER_Surprisingly_Simple_Unsupervised_Video_Instance_Segmentation_CVPR_2024_paper.pdf
- :video_camera: Dual Prototype Attention for UVOS [CVPR2024]: https://openaccess.thecvf.com/content/CVPR2024/papers/Cho_Dual_Prototype_Attention_for_Unsupervised_Video_Object_Segmentation_CVPR_2024_paper.pdf

## Segmentation Benchmark (Dataset)
- Davis
- YouTube-VOS: https://openaccess.thecvf.com/content_ECCV_2018/papers/Ning_Xu_YouTube-VOS_Sequence-to-Sequence_Video_ECCV_2018_paper.pdf
- MOSE https://arxiv.org/pdf/2302.01872
 

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|Franca|o|g/14|61.8|
|DINOv2|o|g/14|63.9|
|Web-DINO|o|7B/14|57.2|
|DINOv3|o|7B/16|71.1|
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# VSS Dataset
- VSPW
![image](https://hackmd.io/_uploads/rkrqaQvkbg.png)
![image](https://hackmd.io/_uploads/S1jhaXvyWe.png)

- VIPSeg Datset

- OVIS

- YTVIS19
- YTVIS21
- Cityscapes , Camvid



# VSPW Benchmark
- mIOU metric
- mIOU-present / short / mid (시간 구간별 정확도) : 특정 시간 간격에 따라 나눠서 mIOU 계산
- mVC8, mVC16: Temporal Consistency (프레임 간 라벨 일관성)

**Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models** [CVPR 2025]
![image](https://hackmd.io/_uploads/Bk6xnY_Jbx.png)



---


# ViCLIP
## MeViS (Motion)
```
  {
    "video_path": "/scratch2/mu06363/cvpr2026/datasets/MeViS/Videos_49/00099fdb8d89_1.mp4",
    "caption": "Two people are dancing hand in hand in a room.",
    "concepts": "person, mirror, bed, shirt, pants, dress",
    "camera": "static",
    "object": "dancing"
  },
```

### 문제점
    
```
video → [T, H, W, 3]  → ViCLIP (image encoder) → video_feature (1 × 512)
text  → "A dog running" → text_feature (1 × 512)
Cosine Simiarity = video feature * text feature
```
    
```
[Frame 1] → Frame Features → Similarity → Heatmap 1
[Frame 2] → Frame Features → Similarity → Heatmap 2
...
[Frame T] → Frame Features → Similarity → Heatmap T
```

### 방법
- Concept에 대해서 sentence 로 "A video of a <concept>"
- `Object` 라고 되어있는 부분에 대한 map "Two people are **dancing** hand in hand in a room."


---