This article explores how modern video AI techniques—ranging from transformer‑based classification to semi‑supervised retrieval and token‑halting acceleration—enable efficient, accurate detection of prohibited content and fast, scalable video search in the era of short‑form media.
The article outlines the Tencent Advertising Algorithm Competition’s video ad challenge, details the paper submission guidelines, and shares a participant’s step‑by‑step technical approach—including baseline experiments, model re‑implementation with Paddle, multimodal feature extraction, optimizer choices, and future improvement directions—providing practical AI insights for multimedia video classification.
This article presents a technical overview of a multimodal AI framework that combines image and audio analysis to identify pornographic video content, detailing model architectures, feature extraction methods, and experimental results achieving 93.4% accuracy on a 3,000‑sample test set.
This article presents an overview of Baidu's video foundation technology architecture, covering the video R&D platform, core AI techniques for video understanding, editing, surveillance, and general vision, and detailing innovations such as Attention‑Cluster networks, cross‑modality attention with graph convolution, GANs, super‑resolution, and adaptive encoding.
The AMAP‑TECH competition challenged participants to infer real‑time road conditions from in‑vehicle video, prompting the authors to combine lane‑wise vehicle detection with LightGBM and later an end‑to‑end DenseNet‑GRU model, augment data, ensemble five networks, and achieve a 0.7237 F1 score while outlining future deployment and research directions.
The winning solution for the AMAP‑TECH competition combines EfficientNet spatial features with ConvLSTM temporal modeling, adds a mask‑prediction branch for closed‑road obstacles, and mitigates severe class imbalance through a two‑stage decoupled training pipeline, achieving first place on leaderboard A and top‑5 on leaderboard B without ensembling.
The talk presents Alibaba’s hierarchical video‑category system and a multimodal classification pipeline—leveraging EfficientNet, NeXtVLAD fusion, attention‑dropping augmentation, and MoCo contrastive learning—that together boost cold‑start recall by 43%, improve program classification over 20%, and set the stage for larger models and advanced unsupervised methods.
This article presents a comprehensive overview of Alibaba Entertainment's category system and multimodal video classification algorithm, detailing the construction of a high‑accuracy hierarchical taxonomy, improvements to image feature extraction using EfficientNet and data augmentation, unsupervised training techniques, experimental results, practical pitfalls, and future research directions.
The article describes iQIYI's multimodal soft‑porn detection system for short videos, covering challenges like subjective definitions and class imbalance, and detailing text (Convolutional Bi‑LSTM), image (Xception‑CBAM), video (NeXtVLAD) models, integration strategies, key takeaways, and future improvements.
The Alibaba iDST team clinched first place in the ACM MM2017 LSVC competition by leveraging Alibaba Cloud’s ODPS to extract eight multimodal features, achieving a 0.8485 mAP on the validation set, and demonstrating the critical role of rich modality fusion in large‑scale video classification.
