We present a scalable approach for learning powerful visual features for emotion recognition. A critical bot- tleneck in emotion recognition is the lack of ...

We use this dataset to train a feature extraction network, EmotionNet, which we further regularize using joint text and visual embedding and text distillation.

Introduction: This repo provides code structure for the experiments in "Learning Visual Emotion Representations From Web Data".

We propose methods to handle noisily, partially anno- tated data, improving visual feature learning through text model distillation and joint visual-text ...

We use this dataset to train a feature extraction network, EmotionNet, which we further regularize using joint text and visual embedding and text distillation.

A scalable approach for learning powerful visual features for emotion recognition using a webly derived large scale dataset, StockEmotion, and achieves ...

Other directions in emotion recognition include estimating valence-arousal (continuous variables) from faces with limited context [71], learning representations ...

May 22, 2023 · We present EmotionCLIP, the first pre-training paradigm to extract visual emotion representations from verbal and nonverbal communication using only uncurated ...

In this paper, we present a large-scale multimodal pre-training method to learn visual emotion representation by aligning emotion, object, and attribute triplet ...

Jul 8, 2024 · In this paper, we propose a novel approach, Multi-Perspective Prompt Learning (MPP-CLIP), within the context of CLIP, for visual emotion analysis.