Popular GitHub Repositories for Expected Improvement

? TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

? GPTSwarm: LLM agents as (Optimizable) Graphs

The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected

based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment C

Wining solution and its improvement for MICCAI 2017 Robotic Instrument Segmentation Sub-Challenge

Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577

Official repository for the paper "A Modern Self-Referential Weight Matrix That Learns to Modify Itself" (ICML 2022 & NeurIPS 2021 Deep RL Workshop) and "Accelerating Neural Self-Improvement via Bootstrapping" (ICLR 2023 Workshop)

Explore concepts like Self-Correct, Self-Refine, Self-Improve, Self-Contradict, Self-Play, and Self-Knowledge, alongside o1-like reasoning elevation? and hallucination alleviation?.

BotSIM - a data-efficient end-to-end Bot SIMulation toolkit for evaluation, diagnosis, and improvement of commercial chatbots

An improvement-based Deep Reinforcement Learning Algorithm presented in paper https://arxiv.org/abs/1912.05784v2 for solving the TSP problem.

[Materials & Design 2024] A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning

OpenVLThinker: An Early Exploration to Vision-Language Reasoning via Iterative Self-Improvement

Official PyTorch Implementation of TransZero (AAAI'22)

Wav2Lip UHQ Improvement with ControlNet 1.1

Learn to use LangGraph to iterate over a message until the expected output.

This repository contains the code for the paper“iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement”

Reference github repository for the paper "Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning". We propose a single-image deblurring network that incorporates the two sub-aperture views into a multitask framework. Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network’s ability to learn to deblur the image. Our experiments show this multi-task strategy achieves +1dB PSNR improvement over state-of-the-art defocus deblurring methods. In addition, our multi-task framework allows accurate DP-view synthesis (e.g., ~ 39dB PSNR) from the single input image. These high-quality DP views can be used for other DP-based applications, such as reflection removal. As part of this effort, we have captured a new dataset of 7,059 high-quali

Learn to build a modular real-time feature pipeline, so you avoid Offline-Online Feature Skew, and your deployed ML models work as expected.

Open source 2D/3D face and body analysis library with toolkit for ML model retraining and generalization improvement.