This tutorial explains how to set up PHP on a Linux server, install necessary drivers, capture a video frame using FFmpeg, and integrate the Fer2013 AI model via a Python script to analyze facial expressions and display the detected emotion on a web page.
The paper introduces MODA, a modular duplex attention multimodal model that addresses severe cross‑modal attention imbalance in existing large multimodal models, proposes a novel attention paradigm and masking scheme, and demonstrates significant performance gains across 21 benchmarks in perception, cognition, and emotion tasks, earning a Spotlight paper at ICML 2025.
This guide walks you through installing necessary drivers and libraries, capturing webcam frames with PHP and FFmpeg, and using the Fer2013 model to recognize facial emotions, providing a complete example that displays both the image and detected mood on a web page.
This tutorial explains how to set up a Linux environment with PHP, install necessary camera drivers and libraries, capture video frames using FFmpeg, and apply the Fer2013 emotion‑recognition model to analyze facial expressions and determine a person's emotional state.
This guide demonstrates how to install necessary Linux drivers and PHP libraries, capture a camera frame with FFmpeg, and integrate the Fer2013 AI model via Python to perform real-time emotion recognition using PHP, complete with sample code.
This tutorial shows how to install necessary drivers, capture a live video frame from a Linux camera using PHP and FFmpeg, and apply the open‑source Fer2013 model via a Python script to recognize human emotions from the captured image.
This tutorial demonstrates how to install necessary Linux drivers and PHP libraries, capture video frames via FFmpeg, and apply the open‑source Fer2013 model to recognize human emotions from camera images using PHP code.
This tutorial explains how to capture live video from a camera with PHP, install necessary Linux drivers, extract frames using FFmpeg, and apply the Fer2013 emotion‑recognition model through a Python script to infer human emotions in real time.
This tutorial demonstrates how to install necessary drivers and libraries, capture live video frames with PHP and FFmpeg, and apply the open‑source Fer2013 model via a Python script to recognize human emotions from facial expressions.
The paper introduces a hybrid curriculum learning framework that tackles emotion shift and confusing labels in emotion recognition in conversation by applying nested curriculum stages at both conversation and utterance levels, enabling a progressive easy‑to‑hard training that markedly boosts classic ERC model performance across four public datasets and is already deployed in Alibaba’s entertainment AI brain script health‑check service.
This article explains how Yuedu's TTS synthesis platform tackles the booming audiobook market by using AI‑driven text preprocessing, role graph construction, content structuring, emotion and effect recognition, and a streamlined post‑processing workflow to efficiently generate multi‑character, emotionally rich audio books at scale.
This article explores the development and application of affective computing in the retail sector, covering its psychological foundations, emotion recognition algorithms for facial expressions, speech, and text, multimodal fusion techniques, market players, and future prospects for enhancing shopper experiences, staff service quality, and sales performance.
This article introduces Alibaba's new e‑book collection of five ICASSP‑accepted papers that showcase advances in speech recognition, synthesis, and emotion detection, detailing novel models like DFSMN, A‑LSTM, and speaker‑adaptation techniques that dramatically improve speed, size, and accuracy.
Meituan's intelligent customer service leverages a dialogue interaction framework with machine‑learning‑driven intent mining, understanding, emotion detection, dialogue management, knowledge discovery, and multi‑task learning to handle both simple QA and complex multi‑turn tasks across its diverse service ecosystem.
