OBJECT DETECTION

Expanding Low-Density Latent Regions for Open-Set Object Detection

OpenDet Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022) Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia. arXiv preprint. OpenDet2: OpenDet is implemented based on detectron2. Setup The code is based on detectron2 v0.5. Here is a from-scratch setup script. conda create -n opendet2 python=3.8 -y conda activate opendet2 conda install pytorch=1.8.1 torchvision cudatoolkit=10.1 -c pytorch -y pip install detectron2==0.5 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html git clone    

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Adaptation

Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution

Paper Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution Jie Liang*, Hui Zeng*, and Lei Zhang. In arxiv preprint. Abstract Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to the unknown complex degradation of real-world images and the limited computation resources in practical applications. Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however, these methods largely rely on heavy backbone networks and they are inflexible to handle images of different […]

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Distributed

A library for building and serving multi-node distributed faiss indices

Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don’t fit into a single server memory. It follows a simple concept of a set of index server processes runing in a complete isolation from each other. All the coordination is done at the client side. This siplified many-vs-many client-to-server relationship architecture is flexible and is specifically designed for research projects vs more complicated solutions that aims mostly at production usage and transactionality support. The […]

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Tool

A tool for evaluating the predictive performance on activity cliff compounds of machine learning models

Molecule Activity Cliff Estimation (MoleculeACE) is a tool for evaluating the predictive performance on activity cliff compounds of machine learning models. MoleculeACE can be used to: Analyze and compare the performance on activity cliffs of machine learning methods typically employed in QSAR. Identify best practices to enhance a model’s predictivity in the presence of activity cliffs. Design guidelines to consider when developing novel QSAR approaches. Benchmark study In a benchmark study we collected and curated bioactivity data on 30 macromolecular […]

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Transformer

BoxeR: Box-Attention for 2D and 3D Transformers

By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-Attention for 2D and 3D Transformers. Introduction TL; DR. BoxeR is a Transformer-based network for end-to-end 2D object detection and instance segmentation, along with 3D object detection. The core of the network is Box-Attention which predicts regions of interest to attend by learning the transformation (translation, scaling, and rotation) from reference windows, yielding competitive performance on several vision […]

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Neural Network

CoNeRF: Controllable Neural Radiance Fields

This is the official implementation for “CoNeRF: Controllable Neural Radiance Fields” The codebase is based on HyperNeRF implemente in JAX, building on JaxNeRF. Setup The code can be run under any environment with Python 3.8 and above. (It may run with lower versions, but we have not tested it). We recommend using Miniconda and setting up an environment: conda create –name conerf python=3.8 Next, install the required packages: pip install -r requirements.txt Install the appropriate JAX distribution    

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Segmentation

Mask Transfiner for High-Quality Instance Segmentation, CVPR 2022

Mask Transfiner for High-Quality Instance Segmentation [Mask Transfiner, CVPR 2022] This is the official pytorch implementation of Transfiner built on the open-source detectron2 [Under Construction]. Mask Transfiner for High-Quality Instance Segmentation Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu CVPR, 2022 Highlights Transfiner: High-quality instance segmentation with state-of-the-art performance and extreme details. Novelty: An efficient transformer targeting for high-resolution instance masks predictions based on the quadtree structure. Efficacy: Large mask and boundary AP improvements on three […]

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End-to-End

End-to-end Point Cloud Correspondences with Transformers

This repository contains the source code for REGTR. REGTR utilizes multiple transformer attention layers to directly predict each downsampled point’s corresponding location in the other point cloud. Unlike typical correspondence-based registration algorithms, the predicted correspondences are clean and do not require an additional RANSAC step. This results in a fast, yet accurate registration. If you find this useful, please cite: @inproceedings{yew2022regtr, title={REGTR: End-to-end Point Cloud Correspondences with Transformers}, author={Yew, Zi Jian and Lee, Gim hee}, booktitle={CVPR}, year={2022}, } Dataset environment […]

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Dataset

Facestar dataset. High quality audio-visual recordings of human conversational speech

Description Existing audio-visual datasets for human speech are either captured in a clean, controlled environment but contain only a small amount of speech data without natural conversations, or are collected in-the-wild with unreliable audio quality, interfering sounds, low face resolution, and unreliable or occluded lip motion. The Facestar dataset aims to enable research on audio-visual modeling in a large-scale and high-quality setting. Core dataset features: 10 hours of high-quality audio-visual speech data audio recordings in a quiet environment at 16kHz […]

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