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Xiamen University, Intel, and DJI Jointly Release GIM: Learning Zero-Shot Image Matching from Internet Videos

Image matching is a fundamental task in computer vision. Recently, matching models based on deep learning have gradually gained popularity. Xiamen University, Intel, and DJI have introduced GIM: Learning Generalizable Image Matcher from Internet Videos. GIM enables matching models to learn strong generalization capabilities from internet videos. The GIM framework is suitable for all matching models, including DKM, LoFTR, and SuperGlue. The authors have proposed the first ...

10.1k 3 days ago
Xiamen University, Intel, and DJI Jointly Release GIM: Learning Zero-Shot Image Matching from Internet Videos

Models

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Hunyuan-Standard

Tencent

Hunyuan-Standard

$0.8

Input tokens/M

$2

Output tokens/M

30

Context Length

GLM-4-Plus

Chatglm

GLM-4-Plus

$100

Input tokens/M

$100

Output tokens/M

128

Context Length

Yi-Large

01-ai

Yi-Large

-

Input tokens/M

-

Output tokens/M

32

Context Length

Superglue_outdoor

magic-leap-community

S

SuperGlue is a graph neural network-based feature matching model for matching interest points in images, suitable for image matching and pose estimation tasks.

Computer VisionTransformersTransformers
magic-leap-community
18.4k
2

Superglue_indoor

magic-leap-community

S

SuperGlue is an image feature matching model based on graph neural networks, which can jointly find correspondences and reject non-matchable points. It is suitable for pose estimation and image matching in challenging indoor and outdoor environments.

Computer VisionTransformersTransformers
magic-leap-community
2.5k
2
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