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Found 9 papers, 2 papers with code
Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval
Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy.
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation
In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains.
Deep Learning for Instance Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively.
Learning a Recurrent Residual Fusion Network for Multimodal Matching
A major challenge in matching between vision and language is that they typically have completely different features and representations.
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Despite recent advances in multi-scale deep representations, their limitations are attributed to expensive parameters and weak fusion modules.
Learning Relaxed Deep Supervision for Better Edge Detection
We consider these false positives in the supervision, and are able to achieve high performance for better edge detection.
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking
We present a head tracker which is a combination of a optical flow and a template based tracker.
