論文/代碼速遞2022.10.13！

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主頁：https://sgvr.kaist.ac.kr/publication/flow-supervisor/ 代碼：https://github.com/iwbn/flow-supervisor

 光流CNN的訓練管道由合成數據集的預訓練階段和目標數據集的微調階段組成。然而，從目標視頻中獲取ground truth 流需要付出巨大的努力。本文提出了一種實用的微調方法，以使預處理模型適應沒有ground truth 流的目標數據集，這種方法尚未得到廣泛的探索。具體來說，我們提出了一個用于自監(jiān)督的流監(jiān)督，它由參數分離和學生輸出連接組成。這種設計的目的是穩(wěn)定收斂，并比在微調任務中不穩(wěn)定的傳統(tǒng)自監(jiān)督方法具有更好的精度。實驗結果表明，與不同的自監(jiān)督方法相比，該方法對于半監(jiān)督學習是有效的。此外，通過利用額外的未標記數據集，我們在Sintel和KITTI基準上對最先進的光流模型進行了有意義的改進

最新論文整理

   ECCV2022

Updated on : 13 Oct 2022

total number : 1

DeepMend: Learning Occupancy Functions to Represent Shape for Repair

• 論文/Paper: http://arxiv.org/pdf/2210.05728

• 代碼/Code: https://github.com/terascale-all-sensing-research-studio/deepmend

    CVPR2022

   NeurIPS

Updated on : 13 Oct 2022

total number : 11

AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars

• 論文/Paper: http://arxiv.org/pdf/2210.06465

• 代碼/Code: None

Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching

• 論文/Paper: http://arxiv.org/pdf/2210.06373

• 代碼/Code: https://github.com/craigleili/AttentiveFMaps

Latency-aware Spatial-wise Dynamic Networks

• 論文/Paper: http://arxiv.org/pdf/2210.06223

• 代碼/Code: https://github.com/leaplabthu/lasnet

Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning

• 論文/Paper: http://arxiv.org/pdf/2210.06044

• 代碼/Code: None

Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning

• 論文/Paper: http://arxiv.org/pdf/2210.06031

• 代碼/Code: https://github.com/microsoft/XPretrain.

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

• 論文/Paper: http://arxiv.org/pdf/2210.05968

• 代碼/Code: https://github.com/sclbd/transfer_attack_rap

Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation

• 論文/Paper: http://arxiv.org/pdf/2210.05941

• 代碼/Code: None

A Lower Bound of Hash Codes' Performance

• 論文/Paper: http://arxiv.org/pdf/2210.05899

• 代碼/Code: https://github.com/vl-group/lbhash

SegViT: Semantic Segmentation with Plain Vision Transformers

• 論文/Paper: http://arxiv.org/pdf/2210.05844

• 代碼/Code: None

Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork

• 論文/Paper: http://arxiv.org/pdf/2210.06428

• 代碼/Code: https://github.com/VITA-Group/Trap-and-Replace-Backdoor-Defense

Towards Theoretically Inspired Neural Initialization Optimization

• 論文/Paper: http://arxiv.org/pdf/2210.05956

• 代碼/Code: https://github.com/HarborYuan/GradCosine