Generative Multiplane Images: Making a 2D GAN 3D-Aware

What is really needed to make an existing 2D GAN 3D-aware? To answer this question, we modify a classical GAN, i.e., StyleGANv2, as little as possible. We find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator.

Github: https://github.com/apple/ml-gmpi

Paper: https://arxiv.org/abs/2207.10642v1

Dataset: https://paperswithcode.com/dataset/metfaces

Project: https://xiaoming-zhao.github.io/projects/gmpi/

Pretrained checkpoints: https://drive.google.com/drive/folders/1MEIjen0XOIW-kxEMfBUONnKYrkRATSR_

👉 @bigdata_1

Machine Learning Algorithms Explained in Less Than 1 Minute Each

https://www.kdnuggets.com/2022/07/machine-learning-algorithms-explained-less-1-minute.html

👉 @bigdata_1

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.

Github: https://github.com/164140757/scm

Paper: https://arxiv.org/abs/2207.10447v1

Dataset: https://paperswithcode.com/dataset/cub-200-2011

👉 @bigdata_1

SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks

Simple but very effective attention module for Convolutional Neural Networks (ConvNets).

Github: https://github.com/ZjjConan/SimAM

Paper: http://proceedings.mlr.press/v139/yang21o.html

Dataset: https://paperswithcode.com/dataset/cifar-10

Google Drive: https://drive.google.com/drive/folders/1rRT0UCPeRLPdTCJvv43hvAnGnS49nIWn?usp=sharing

👉 @bigdata_1

Multiface: A Dataset for Neural Face Rendering

Github: https://github.com/facebookresearch/multiface

Paper: https://arxiv.org/abs/2207.11243v1

Dataset: https://paperswithcode.com/dataset/facewarehouse

👉 @bigdata_1

MAPIE - Model Agnostic Prediction Interval Estimator

MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourite scikit-learn-compatible model for single-output regression or multi-class classification settings.

Github: https://github.com/scikit-learn-contrib/mapie

Paper: https://arxiv.org/abs/2207.12274v1

Docs: https://mapie.readthedocs.io/en/latest/

👉 @bigdata_1

Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training

Github: https://github.com/hxyou/msclip

Paper: https://arxiv.org/abs/2207.12661v1

Dataset: https://paperswithcode.com/dataset/sst

👉 @bigdata_1

Learning Protein Representations via Complete 3D Graph Networks

DIG: Dive into Graphs is a turnkey library for graph deep learning research.

Github: https://github.com/divelab/DIG

Paper: https://arxiv.org/abs/2207.12600v1

Tutorials: https://diveintographs.readthedocs.io/en/latest/tutorials/graphdf.html

Documentation: https://diveintographs.readthedocs.io/

Benchmarks: https://github.com/divelab/DIG/tree/dig-stable/benchmarks

Dataset: https://paperswithcode.com/dataset/atom3d

👉 @bigdata_1

ALBench: A Framework for Evaluating Active Learning in Object Detection

An active learning benchmark framework named as ALBench for evaluating active learning in object detection.

Github: https://github.com/industryessentials/ymir

Paper: https://arxiv.org/abs/2207.13339v1

Projects: https://github.com/IndustryEssentials/ymir/projects

Dataset: https://paperswithcode.com/dataset/coco

👉 @bigdata_1

Deep Deformable 3D Caricature with Learned Shape Control (DD3C)

Github: https://github.com/ycjungsubhuman/deepdeformable3dcaricatures

Paper: https://arxiv.org/abs/2207.14593v1

Project: https://ycjungsubhuman.github.io/DeepDeformable3DCaricatures

Dataset: https://paperswithcode.com/dataset/facewarehouse

Video: https://youtu.be/WLMPEaK6E4M

👉 @bigdata_1

CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation

Github: https://github.com/huawei-noah/noah-research/tree/master/CLIFF

Paper: https://arxiv.org/abs/2208.00571v1

Pretrained checkpoints : https://drive.google.com/drive/folders/1EmSZwaDULhT9m1VvH7YOpCXwBWgYrgwP

Dataset: https://paperswithcode.com/dataset/human3-6m

👉 @bigdata_1

Expanding Language-Image Pretrained Models for General Video Recognition by Microsoft.

Video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts.

Github: https://github.com/microsoft/VideoX/tree/master/X-CLIP

Paper: https://arxiv.org/abs/2208.02816v1

Dataset: https://paperswithcode.com/dataset/ucf101

👉 @bigdata_1

Prompt Tuning for Generative Multimodal Pretrained Models

Github: https://github.com/ofa-sys/ofa

Paper: https://arxiv.org/abs/2208.02532v1

Dataset: https://paperswithcode.com/dataset/snli-ve

Demo: https://huggingface.co/spaces/OFA-Sys/OFA-Generic_Interface

👉 @bigdata_1

P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting

Github: https://github.com/wangzy22/P2P

Paper: https://arxiv.org/abs/2208.02812v1

Dataset: https://paperswithcode.com/dataset/imagenet

Model: https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip

👉 @bigdata_1

Per-Clip Video Object Segmentation

Progressive matching mechanism for efficient information-passing within a clip.

Github: https://github.com/pkyong95/PCVOS

Paper: https://arxiv.org/abs/2208.01924v1

Dataset: https://paperswithcode.com/dataset/davis

Video: https://youtu.be/6QATHDwrUx0

👉 @bigdata_1

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

Github: https://github.com/lkeab/BCNet

Paper: https://arxiv.org/abs/2208.04438v1

Dataset: https://paperswithcode.com/dataset/bdd100k

Video: https://www.youtube.com/watch?v=iHlGJppJGiQ

👉 @bigdata_1

LAMDA-SSL: Semi-Supervised Learning in Python


Github: https://github.com/ygzwqzd/lamda-ssl

Paper: https://arxiv.org/pdf/2208.04610.pdf

Docs: https://ygzwqzd.github.io/LAMDA-SSL

👉 @bigdata_1