👨 Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance

Github: https://github.com/aashishrai3799/3DFaceCAM

Project: https://aashishrai3799.github.io/3DFaceCAM

Paper: https://arxiv.org/abs/2208.14263

Video: https://drive.google.com/file/d/1PqIN4Rzp4vapWs2pUegUEoMhg4lM2Smy/view?usp=sharing

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

@ArtificialIntelligencedl

🏙 AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System

Github: https://github.com/billyblu2000/accomontage2

Paper: https://arxiv.org/abs/2209.00353v1

Dataset: https://drive.google.com/drive/folders/1z8oW16dZtdS06woHc7_rxserNJRrkc4s?usp=sharing

@ArtificialIntelligencedl

💫 AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System

AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melod

Github: https://github.com/billyblu2000/accomontage2

Paper: https://arxiv.org/abs/2209.00353v1

Dataset: https://drive.google.com/drive/folders/1z8oW16dZtdS06woHc7_rxserNJRrkc4s?usp=sharing

@ArtificialIntelligencedl

🔘 Real-time 3D Single Object Tracking with Transformer

Github: https://github.com/shanjiayao/ptt

Paper: https://arxiv.org/abs/2209.00860v1

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

Video: https://youtu.be/Cajj6iHFvrc

@ArtificialIntelligencedl

📍 Structural Bias for Aspect Sentiment Triplet Extraction

Github: https://github.com/genezc/structbias

Paper: https://arxiv.org/abs/2209.00820v1

@ArtificialIntelligencedl

⚡️ Continual Learning: Fast and Slow

Dual Networks a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation.

Github: https://github.com/phquang/DualNet

Paper: https://arxiv.org/abs/2209.02370v1

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

@ArtificialIntelligencedl

🧠 Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

Github: https://github.com/amigolab/synthanatomy

Paper: https://arxiv.org/abs/2209.03177v1

Project: https://amigos.ai/thisbraindoesnotexist/

@ArtificialIntelligencedl

🔥 YOLOv6

YOLOv6-N hits 35.9% AP on COCO dataset with 1234 FPS on T4. YOLOv6-S strikes 43.5% AP with 495 FPS, and the quantized YOLOv6-S model achieves 43.3% AP at a accelerated speed of 869 FPS on T4.

git clone https://github.com/meituan/YOLOv6
cd YOLOv6
pip install -r requirements.txt

⚙️ Github
➡️ Paper
✔️ Colab
💻 Quantization Tutorial
📄 Dataset

@ai_machinelearning_big_data

💬 Text-Free Learning of a Natural Language Interface for Pretrained Face Generators

Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis.

pip install git+https://github.com/openai/CLIP.git

Github: https://github.com/duxiaodan/fast_text2stylegan

Paper: https://arxiv.org/abs/2209.03177v1

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

@ArtificialIntelligencedl

💬 AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog

git clone https://github.com/Tomiinek/Aargh.git
cd Aargh
pip install -e .

Github: https://github.com/tomiinek/aargh

Paper: https://arxiv.org/abs/2209.03632v1

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

@ArtificialIntelligencedl

🔭 AiRLoc: Aerial View Goal Localization with Reinforcement Learning

conda create -n airloc
conda activate airloc
pip install -r requirements.txt

Github: https://github.com/aleksispi/airloc

Paper: https://arxiv.org/abs/2209.03694v1

@ArtificialIntelligencedl

MassMIND: Massachusetts Maritime INfrared Dataset 1

Github: https://github.com/uml-marine-robotics/massmind

Paper: https://arxiv.org/abs/2209.04097v1

Dataset: https://drive.google.com/file/d/1T572f0oqy5JmuTvVEwkSUeXLWOSHl4hL/view

@ArtificialIntelligencedl

💫 F-COREF: Fast, Accurate and Easy to Use Coreference Resolution

a python package for fast, accurate, and easy-to-use English coreference resolution.

pip install fastcoref

Github: https://github.com/shon-otmazgin/fastcoref

Paper: https://arxiv.org/abs/2209.04280v2

Dataset: https://paperswithcode.com/dataset/multi-news

@ArtificialIntelligencedl

🦾 StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation

Github: https://github.com/adymaharana/storydalle

Paper: https://arxiv.org/abs/2209.06192

Model: https://github.com/adymaharana/storydalle/blob/main/MODEL_CARD.MD

Demo: https://github.com/adymaharana/storydalle/blob/main/DEMO.MD

Dataset: https://paperswithcode.com/dataset/multi-news

@ArtificialIntelligencedl

🚀 Entity Tagging: Extracting Entities in Text Without Mention Supervision

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

Paper: https://arxiv.org/abs/2209.06148v1

Model: https://github.com/adymaharana/storydalle/blob/main/MODEL_CARD.MD

Dataset: https://manikvarma.org/downloads/XC/XMLRepository.html

@ArtificialIntelligencedl

🛠 CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment

Github: https://github.com/microsoft/xpretrain

Paper: https://arxiv.org/abs/2209.06430v1

Dataset: https://paperswithcode.com/dataset/flickr30k

@ArtificialIntelligencedl

📲 Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation

Github: https://github.com/ZM-Zhou/SMDE-Pytorch

Paper: https://arxiv.org/abs/2209.07088v1

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

@ArtificialIntelligencedl

🛠 Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?

⚙️Github: https://github.com/VITA-Group/Simple3D-Former

📄Paper: https://arxiv.org/abs/2209.07026v1

📎Dataset: https://paperswithcode.com/dataset/modelnet

@ArtificialIntelligencedl

🖊 Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

git clone https://github.com/mmasana/FACIL.git
cd FACIL

⚙️Github: https://github.com/mmasana/FACIL

📄Paper: https://arxiv.org/abs/2209.08010v1

📎Dataset: https://github.com/mmasana/FACIL/blob/master/src/datasets#datasets

@ArtificialIntelligencedl

🔌 HiPart: Hierarchical divisive clustering toolbox

It is a package with similar execution principles as the scikit-learn package. It also provides two types of static visualizations for all the algorithms executed in the package, with the addition of linkage generation for the divisive hierarchical clustering structure.

pip install HiPart

⚙️Github: https://github.com/panagiotisanagnostou/hipart

📄Paper: https://arxiv.org/abs/2209.08680v1

📎Dataset: https://paperswithcode.com/dataset/usps

@ArtificialIntelligencedl