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Found 19 papers, 6 papers with code
Forgedit: Text Guided Image Editing via Learning and Forgetting
Text-guided image editing on real or synthetic images, given only the original image itself and the target text prompt as inputs, is a very general and challenging task.
Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion
Then, a denoising stage removes the noise in the fused frame.
Ranked #3 on
Video Denoising
on CRVD
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena.
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied.
Learning Temporal Point Processes via Reinforcement Learning
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes
To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93. 3$\%$.
Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing
Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i. e., by taking advantage of the monotonicity).
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
Finding optimal configurations in a geometric space is a key challenge in many technological disciplines.
Image to Multi-Modal Retrieval for Industrial Scenarios
We formally define a novel valuable information retrieval task: image-to-multi-modal-retrieval (IMMR), where the query is an image and the doc is an entity with both image and textual description.
Cross-domain Augmentation Networks for Click-Through Rate Prediction
The translation network is able to compute features from two domains with heterogeneous inputs separately by designing two independent branches, and then learn meaningful cross-domain knowledge using a designed cross-supervised feature translator.
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business.
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Later the augmentation network employs the explicit cross-domain knowledge as augmented information to boost the target domain CTR prediction.
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance.
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
Cell Variational Information Bottleneck Network
In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end training method.
