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Found 10 papers, 6 papers with code
Unsupervised Learning of Optical Flow with Deep Feature Similarity
Deep unsupervised learning for optical flow has been proposed, where the loss measures image similarity with the warping function parameterized by estimated flow.
SemCity: Semantic Scene Generation with Triplane Diffusion
In this paper, we concentrate on generating a real-outdoor scene through learning a diffusion model on a real-world outdoor dataset.
Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data
To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale.
Semi-Supervised Learning of Optical Flow by Flow Supervisor
A training pipeline for optical flow CNNs consists of a pretraining stage on a synthetic dataset followed by a fine tuning stage on a target dataset.
In-N-Out: Towards Good Initialization for Inpainting and Outpainting
Our self-supervision method, In-N-Out, is summarized as a training approach that leverages the knowledge of the opposite task into the target model.
Two-stream Spatiotemporal Feature for Video QA Task
Understanding the content of videos is one of the core techniques for developing various helpful applications in the real world, such as recognizing various human actions for surveillance systems or customer behavior analysis in an autonomous shop.
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
Up to now, only limited research has been conducted on cross-modal retrieval of suitable music for a specified video or vice versa.
Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments
A fully connected layer is used to select the key frames and key segments, while the convolutional layer is used to describe them.
SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training.
Image-Text Multi-Modal Representation Learning by Adversarial Backpropagation
We present novel method for image-text multi-modal representation learning.
