This paper describes our submission for task 5 Multimedia Automatic Misogyny Identification (MAMI) at SemEval-2022.
The memes serve as an important tool in online communication, whereas some hateful memes endanger cyberspace by attacking certain people or subjects.
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.
This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.
Rather than end-to-end learning, most existing methods adopt a head-only learning paradigm, where the video encoder is pre-trained for action classification, and only the detection head upon the encoder is optimized for TAD.
Ranked #5 on Temporal Action Localization on THUMOS’14
In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition.
Temporal action detection (TAD) aims to determine the semantic label and the temporal interval of every action instance in an untrimmed video.
Ranked #1 on Temporal Action Localization on HACS
Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0. 73 mm RMS error in clean environments and 2. 72 mm RMS error in challenging environments with blood and occlusion.
Current developments in temporal event or action localization usually target actions captured by a single camera.
Ranked #1 on Temporal Action Localization on MUSES
Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes.