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CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework, Wentao Wu, Xiao Wang, Chenglong Li, Bo Jiang, Jin Tang, Bin Luo, Qi Liu
Feedback & wiki for Snipaste https://snipaste.com
[PR 2025] Region-aware mutual relational knowledge distillation for semantic segmentation
Event camera (DVS, Spike) based Papers Published on Top International Conference
The official project website of "Augmentation-free Dense Contrastive Distillation for Efficient Semantic Segmentation" (Af-DCD for short, accepted to NeurIPS 2023).
mobilenetv3 with pytorch，provide pre-train model
Official implementation of "Stereo Depth from Events Cameras: Concentrate and Focus on the Future" (CVPR 2022)
Official code for "Temporal Event Stereo via Joint Learning with Stereoscopic Flow" (ECCV2024)
Code for the NeurIPS 2024 paper [Zero-Shot Event-Intensity Asymmetric Stereo via Visual Prompting from Image Domain].
[CVPR-2022] Official implementations of CIRKD: Cross-Image Relational Knowledge Distillation for Semantic Segmentation and implementations on Cityscapes, ADE20K, COCO-Stuff., Pascal VOC and CamVid.
[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
Self-supervised monocular depth estimation with a vision transformer
[CVPR 2022] MPViT:Multi-Path Vision Transformer for Dense Prediction
[AAAI 2021] HR-Depth : High Resolution Self-Supervised Depth Esti 8FBC mation
[CVPR2023] Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
official implementation of "Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries"
Paper Reproduction for "Learning Monocular Dense Depth from Events" | CS4245 Computer Vision by Deep Learning course project
An open source implementation of CLIP.
Denoising Diffusion Probabilistic Models