VLM Series - Object Recognition as Next Token Prediction - Paper Interpretation - Code World

VLM Series - Object Recognition as Next Token Prediction - Paper Interpretation

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VLM Series - Object Recognition as Next Token Prediction - Paper Interpretation

AIGC (MLLM, VLM, LLM, SD) series - paper interpretation directory

[Interpretation of the paper] Camouflaged Object Detection

Paper interpretation | YOLO series pioneering work: unified real-time object detection

Text recognition-SVTR paper interpretation

Interpretation of the paper--Visual Lane Tracking and Prediction for Autonomous Vehicles

Interpretation of the paper: High-quality object tracking

AIGC series: Vision Transformer principle and paper interpretation

AIGC series: ControlNet principles and paper interpretation

(Paper Reading 26-27) Object Recognition

Interpretation of the paper: End-to-End Object Detection with Transformers

[YOLOX paper + source code interpretation] YOLOX: Exceeding YOLO Series in 2021

[Paper Interpretation Series] NER Direction: LatticeLSTM (ACL2018)

Look at the paper series (1) - Interpretation of Densely Connected Convolutional Networks (DenseNet)

[Interpretation of the paper] Graph-based self-supervised learning joint embedding prediction architecture

Detailed interpretation of multi-feature LSTM time series prediction code based on pytorch (with complete code)

Interpretation of the paper X-CLIP : Expanding Language-Image Pretrained Models for General Video Recognition

Vision-based instrument detection/pointer instrument automatic recognition of readings——interpretation of the paper

Paper's EfficientDet: Translation and Interpretation of "Scalable and Efficient Object Detection—Scalable and Efficient Object Detection"—Sequel

[Target Recognition-Paper Notes]Object Detection in Videos by Short and Long Range Object Linking

The object recognition image recognition

[Target detection] Target detection meets knowledge graph: Interpretation and reproduction of Object detection meets knowledge graphs paper

Paper Interpretation--K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions

Interpretation of "You Only Look Once: Unified, Real-Time Object Detection" YOLOV1 paper

Interpretation of the paper｜VoxelNet: End-to-end learning for point cloud-based 3D object detection

ICCV 2023 Random Boxes Are Open-world Object Detectors Paper Interpretation

Paper Interpretation | Center-based 3D Object Detection and Tracking

MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection Paper Interpretation

Paper interpretation | Original PV-RCNN network for 3D object detection

【Paper Interpretation】Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking

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