tfvaegan[ECCV 2020] Official Pytorch implementation for "Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification". SOTA results for ZSL and GZSL
Stars: ✭ 107 (+0%)
WS3DOfficial version of 'Weakly Supervised 3D object detection from Lidar Point Cloud'(ECCV2020)
Stars: ✭ 104 (-2.8%)
DeepPS[ECCV 2020] "Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches"
Stars: ✭ 63 (-41.12%)
SAN[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images
Stars: ✭ 41 (-61.68%)
IAST-ECCV2020IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm
Stars: ✭ 84 (-21.5%)
Point2MeshMeshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance (ECCV2020)
Stars: ✭ 61 (-42.99%)
deep-atrous-guided-filterDeep Atrous Guided Filter for Image Restoration in Under Display Cameras (UDC Challenge, ECCV 2020).
Stars: ✭ 32 (-70.09%)
SRResCycGANCode repo for "Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution" (ECCVW AIM2020).
Stars: ✭ 47 (-56.07%)
dmfontOfficial PyTorch implementation of DM-Font (ECCV 2020)
Stars: ✭ 110 (+2.8%)
MCIS wsssCode for ECCV 2020 paper (oral): Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
Stars: ✭ 151 (+41.12%)
Indoor-SfMLearner[ECCV'20] Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
Stars: ✭ 115 (+7.48%)
SOLARPyTorch code for "SOLAR: Second-Order Loss and Attention for Image Retrieval". In ECCV 2020
Stars: ✭ 150 (+40.19%)
SPANSemantics-guided Part Attention Network (ECCV 2020 Oral)
Stars: ✭ 19 (-82.24%)
softpoolSoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification - ECCV 2020 oral
Stars: ✭ 62 (-42.06%)
LaBERTA length-controllable and non-autoregressive image captioning model.
Stars: ✭ 50 (-53.27%)
visdialVisual Dialog: Light-weight Transformer for Many Inputs (ECCV 2020)
Stars: ✭ 27 (-74.77%)
People-FlowsThe code for our ECCV 2020 paper: Estimating People Flows to Better Count Them in Crowded Scenes
Stars: ✭ 44 (-58.88%)