MD-VQA: Multi-Dimensional No-Reference Video Quality Assessment for CVPR NTIRE 2023

Alibaba’s Taobao VQA team won the CVPR NTIRE 2023 Video Enhancement Challenge by introducing MD‑VQA, a multi‑dimensional no‑reference video quality model that combines a Swin‑Transformer‑V2 spatial backbone, a pre‑trained SlowFast motion encoder, and a convolutional fusion module, pre‑trained on LSVQ, fine‑tuned on NTIRE data, and augmented spatio‑temporally, achieving state‑of‑the‑art SROCC and PLCC scores and now powering quality monitoring on Alibaba’s live‑streaming and short‑video services.

MultimediaNo-ReferenceSwin Transformer

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MD-VQA: Multi-Dimensional No-Reference Video Quality Assessment for CVPR NTIRE 2023

Baidu Geek Talk

Jan 16, 2023 · Artificial Intelligence

Boosting Swin Transformer Speed: Profiling, Mixed Precision, and Kernel Fusion Secrets

This technical walkthrough explains how Swin Transformer training and inference can be dramatically accelerated on NVIDIA GPUs by using Nsight Systems profiling, mixed‑precision tensor‑core kernels, Apex‑based and custom CUDA operator fusion, half2 vectorization, register‑array caching, and INT8 quantization, achieving up to 2.85× training and 7.34× inference speedups while preserving model accuracy.

GPU performanceINT8 QuantizationNsight Profiling

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Boosting Swin Transformer Speed: Profiling, Mixed Precision, and Kernel Fusion Secrets

Baidu Intelligent Cloud Tech Hub

Dec 29, 2022 · Artificial Intelligence

Boost Swin Transformer Speed: Profiling, Mixed Precision, and Operator Fusion Techniques

This article details how to use NVIDIA profiling tools, mixed‑precision training, operator fusion, kernel optimizations, and INT8 quantization to identify and eliminate performance bottlenecks in Swin Transformer models, achieving up to 2.85× training speedup and up to 7.34× inference acceleration on modern GPUs.

AI PerformanceGPU OptimizationOperator fusion

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Boost Swin Transformer Speed: Profiling, Mixed Precision, and Operator Fusion Techniques

HaoDF Tech Team

Oct 8, 2022 · Artificial Intelligence

Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com

This article introduces the Transformer architecture, explains its attention mechanism, details its adaptations for natural language processing, computer vision, and OCR tasks, and presents experimental results of various models such as BERT, ELECTRA, Swin Transformer, and CRNN-BCN on large-scale medical data from Haodf.com.

Model EvaluationNLPOCR

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Exploring Transformer Technology and Its Applications in NLP, Computer Vision, and OCR at Haodf.com

JD Cloud Developers

Apr 19, 2021 · Artificial Intelligence

This Week’s Tech Highlights: SpaceX Launch, AI Transformers, Cloud Servers & More

The developer community tech weekly covers SpaceX's static fire test for a crewed launch, Facebook's new social audio suite, JD Cloud's next‑generation servers with Seagate, Google's postponement of offline PWA support, NVIDIA's first CPU and AI hardware, Adobe co‑founder Charles Geschke's passing, Beijing's intelligent‑connected vehicle pilot zone, the breakthrough Swin Transformer model, and a geometry‑stable method for 6‑DoF object pose estimation.

Artificial IntelligenceSpaceXSwin Transformer

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This Week’s Tech Highlights: SpaceX Launch, AI Transformers, Cloud Servers & More