CVPR2022论文速递!共18篇(2022.3.9)
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2022-03-16 08:39
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
论文/Paper:https://arxiv.org/abs/2203.04287
代码/Code:
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
论文/Paper:https://arxiv.org/abs/2203.04279
代码/Code:https://github.com/PruneTruong/DenseMatching
End-to-End Semi-Supervised Learning for Video Action Detection
论文/Paper:https://arxiv.org/abs/2203.04251
代码/Code:
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object
论文/Paper:https://arxiv.org/abs/2203.04229
代码/Code:https://github.com/manycore-research/faceformer
主页:https://manycore-research.github.io/faceformer/
Selective-Supervised Contrastive Learning with Noisy Labels
论文/Paper:https://arxiv.org/abs/2203.04181
代码/Code:https://github.com/ShikunLi/Sel-CL
Motron: Multimodal Probabilistic Human Motion Forecasting
论文/Paper:https://arxiv.org/abs/2203.04132
代码/Code:
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation
论文/Paper:https://arxiv.org/abs/2203.04074
代码/Code:https://github.com/zhang-tao-whu/e2ec
Shape-invariant 3D Adversarial Point Clouds
论文/Paper:https://arxiv.org/abs/2203.04041
代码/Code:https://github.com/shikiw/SI-Adv
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
论文/Paper:https://arxiv.org/abs/2203.03996
代码/Code:
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
论文/Paper:https://arxiv.org/abs/2203.03962
代码/Code:
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation
论文/Paper:https://arxiv.org/abs/2203.03888
代码/Code:
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
论文/Paper:https://arxiv.org/abs/2203.03884
代码/Code:
Weakly Supervised Semantic Segmentation using Out-of-Distribution Data
论文/Paper:https://arxiv.org/abs/2203.03860
代码/Code:
Deep Rectangling for Image Stitching: A Learning Baseline
论文/Paper:https://arxiv.org/abs/2203.03831
代码/Code:https://github.com/nie-lang/DeepRectangling
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
论文/Paper:https://arxiv.org/abs/2203.03818
代码/Code:
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild
论文/Paper:https://arxiv.org/abs/2203.03800
代码/Code:https://github.com/deeplearning-wisc/stud
On Generalizing Beyond Domains in Cross-Domain Continual Learning
论文/Paper:https://arxiv.org/abs/2203.03970
代码/Code:
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
论文/Paper:https://arxiv.org/abs/2203.03814
代码/Code: