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

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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

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🎯 Object-Compositional Neural Implicit Surfaces

Github: https://github.com/qianyiwu/objsdf

Paper: https://arxiv.org/abs/2207.09686v1

Project: https://qianyiwu.github.io/objectsdf/

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

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

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Distance Learner: Incorporating Manifold Prior to Model Training

Github: https://github.com/microsoft/distance-learner

Paper: https://arxiv.org/abs/2207.06888v1

Project: https://fast-vid2vid.github.io/

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🚀 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

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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

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⚡️ 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

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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

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📝 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/

🔧 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

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✨ 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

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🧿 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_

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🏵 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

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🌟 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

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👱‍♂️ 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

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☀️ 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/

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🔥 Practical Course Deep Learning

A 🚀free course from fast.ai learn how to apply deep learning and machine learning to practical problems.

Course
Lesson 1
Git

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🦾 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

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