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Found 17 papers, 8 papers with code
Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition
Panoramic Activity Recognition (PAR) seeks to identify diverse human activities across different scales, from individual actions to social group and global activities in crowded panoramic scenes.
Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions
The proposed model is based on a conditional generative adversarial network (cGAN) with modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths.
Modality Mixer Exploiting Complementary Information for Multi-modal Action Recognition
CFEM incorporates sepearte learnable query embeddings for each modality, which guide CFEM to extract complementary information and global action content from the other modalities.
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data.
Reinforcement Learning for Supply Chain Attacks Against Frequency and Voltage Control
The ongoing modernization of the power system, involving new equipment installations and upgrades, exposes the power system to the introduction of malware into its operation through supply chain attacks.
Audio-Visual Glance Network for Efficient Video Recognition
To address this issue, we propose Audio-Visual Glance Network (AVGN), which leverages the commonly available audio and visual modalities to efficiently process the spatio-temporally important parts of a video.
Sketch-based Video Object Localization
We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch.
Towards Good Practices for Missing Modality Robust Action Recognition
We ask: how can we train a model that is robust to missing modalities?
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning
In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug.
Modality Mixer for Multi-modal Action Recognition
In multi-modal action recognition, it is important to consider not only the complementary nature of different modalities but also global action content.
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks
Predicting both accurate and reliable solubility values has long been a crucial but challenging task.
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos
To our surprise, we found that training schedule shows divide-and-conquer-like pattern: time segments are first diversified regardless of the target, then coupled with each target, and fine-tuned to the target again.
Learning to Discriminate Information for Online Action Detection: Analysis and Application
To overcome this problem, we propose a novel recurrent unit, named Information Discrimination Unit (IDU), which explicitly discriminates the information relevancy between an ongoing action and others to decide whether to accumulate the input information.
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module.
SRG: Snippet Relatedness-based Temporal Action Proposal Generator
Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate boundaries.
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
Since the cost of labeling increases dramatically as the number of cameras increases, it is difficult to apply the re-identification algorithm to a large camera network.
A Global-Local Emebdding Module for Fashion Landmark Detection
To that end, in this paper, we propose a fashion landmark detection network with a global-local embedding module.
