Computer Vision |

Alibaba Group - Tmall Campus

Sun, 01 Mar 2026 00:00:00 +0000

Overview

Algorithm Intern Mar. 2026 – Apr. 2026

Portrait-to-Anime Generation System

Developed portrait stylization models based on StyleGAN2 and diffusion architectures for high-quality anime-style image generation.
Constructed end-to-end training workflows including dataset preprocessing, data augmentation, model training, inference, and quantitative evaluation.
Built scalable pipelines supporting rapid experimentation across multiple generative model architectures.

Model Optimization and Training Stability

Improved training stability and convergence performance using PyTorch and TensorFlow through optimizer tuning, learning-rate scheduling, and architecture refinement.
Conducted systematic hyperparameter exploration to improve image fidelity, visual quality, and generation consistency.
Evaluated generation performance using both objective metrics and human perceptual assessments.

Representation Enhancement

Explored attention-based feature enhancement and LoRA-based parameter-efficient adaptation techniques for controllable style transfer.
Improved preservation of facial identity features while maintaining target anime-style characteristics.
Enhanced style consistency and fine-grained visual details across generated outputs.

Experimental Platform Development

Designed reproducible experiment management pipelines covering data processing, training, inference, and result analysis.
Automated model evaluation and visualization workflows to support efficient iteration and comparison of generative model variants.

Object Detection in Counter-Strike 2

Mon, 22 Jan 2024 00:00:00 +0000

Team

YiFan Cai (leader)
XiHe Yu
Yu Shi

Overview

This project investigates the application of real-time object detection in Counter-Strike 2 (CS2). We build a custom in-game dataset and systematically compare multiple detection frameworks to evaluate their accuracy, speed, and practicality in dynamic gaming scenarios. Beyond benchmarking, we demonstrate how detection results can be integrated into gameplay-related applications such as distance estimation and automated aiming.

Methods

We implement and compare YOLOv7, Faster R-CNN, and SSD. Key components include:

-Construction of a custom VOC-style CS2 dataset from gameplay footage

-Model training and evaluation under identical settings

-Preprocessing and data augmentation for difficult scenes

-Ensemble-style bounding box fusion using confidence weighting and clustering

Key Features

-Custom-built CS2 object detection dataset

-Comparative study of one-stage vs. two-stage detectors

-Model ensemble via weighted bounding box averaging

-Real-time in-game deployment with screen capture

-Applications including auto-aiming and target distance estimation

YesAI Lab, ShanghaiTech University

Thu, 01 Sep 2022 00:00:00 +0000

Overview

Research Member -ShanghaiTech University, 2022 – 2025*

Generative Modeling Research

Conducted research on diffusion-based generative models for image synthesis, image restoration, and controllable generation.
Participated in projects spanning computer vision and small-molecule generation under structured optimization objectives.
Explored model architecture design, training strategies, and generation-quality enhancement techniques.

Academic Research Workflow

Participated in the complete lifecycle of top-tier conference publications, including literature review, problem formulation, model design, experimentation, and manuscript preparation.
Co-authored a peer-reviewed publication as the third author.
Accumulated three years of continuous research experience in generative modeling and deep learning.

Experimental Development

Reproduced and extended recent research papers in diffusion models and generative learning.
Designed model improvements and conducted hyperparameter optimization for training stability and generation quality.
Performed systematic analysis of model convergence behavior and output fidelity.