Instance Shadow Detection with A Single-Stage Detector

Deep framework, and an evaluation metric to approach this new task.

Github: https://github.com/stevewongv/InstanceShadowDetection

Instance Shadow Detection: https://github.com/stevewongv/SSIS

Video: https://www.youtube.com/watch?v=p0b_2SsFypw

Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing

Paper: https://arxiv.org/abs/2207.04614v1

Datasets: https://paperswithcode.com/dataset/soba

👉 @bigdata_1

Class-incremental Novel Class Discovery

Github: https://github.com/oatmealliu/class-incd

Paper: https://arxiv.org/abs/2207.08605v1

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

👉 @bigdata_1

KGI (Knowledge Graph Induction) for slot filling

Github: https://github.com/ibm/kgi-slot-filling

KILT data and knowledge source: https://github.com/facebookresearch/KILT

Paper: https://arxiv.org/abs/2207.06300v1

Dataset: https://paperswithcode.com/dataset/natural-questions

👉 @bigdata_1

Benchmarking Omni-Vision Representation through the Lens of Visual Realms

Github: https://github.com/ZhangYuanhan-AI/OmniBenchmark

Project: https://zhangyuanhan-ai.github.io/OmniBenchmark

Paper: https://arxiv.org/abs/2207.07106v1

Competition: https://codalab.lisn.upsaclay.fr/competitions/6043

👉 @bigdata_1

Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting

Github: https://github.com/hikopensource/davar-lab-ocr

Paper: https://arxiv.org/abs/2207.06694v1

Dataset: https://paperswithcode.com/dataset/total-text

👉 @bigdata_1

Language Modelling with Pixels

PIXEL is a language model that operates on text rendered as images, fully removing the need for a fixed vocabulary.

Github: https://github.com/xplip/pixel

Paper: https://arxiv.org/abs/2207.06991v1

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

Pretrained: https://huggingface.co/Team-PIXEL/pixel-base

👉 @bigdata_1

CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS

Github: https://github.com/walkerning/aw_nas

Paper: https://arxiv.org/abs/2207.07868v1

Dataset: https://paperswithcode.com/dataset/nas-bench-201

👉 @bigdata_1

HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

Github: https://github.com/amirhossein-kz/hiformer

Paper: https://arxiv.org/abs/2207.08518v1

Tasks: https://paperswithcode.com/task/medical-image-segmentation

👉 @bigdata_1

Automated Crossword Solving

Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.

Github: https://github.com/albertkx/berkeley-crossword-solver

Paper: https://arxiv.org/abs/2205.09665v1

Dataset: https://www.xwordinfo.com/JSON/

👉 @bigdata_1

FSD: Fully Sparse 3D Object Detection & SST: Single-stride Sparse Transformer

Github: https://github.com/tusimple/sst

Paper: http://arxiv.org/abs/2207.10035

Dataset: https://paperswithcode.com/dataset/waymo-open-dataset

👉 @bigdata_1

Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification

Tip-Adapter is a training-free adaption method for CLIP to conduct few-shot classification.

Github: https://github.com/gaopengcuhk/tip-adapter

Paper: https://arxiv.org/abs/2207.09519v1

Dataset: https://paperswithcode.com/dataset/oxford-102-flower

👉 @bigdata_1

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