Search Results for author:
Found 29 papers, 9 papers with code
TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state
In long-term time series forecasting, different variables often influence the target variable over distinct time intervals, a challenge known as the multi-delay issue.
MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework
Recent advancements in large language models (LLMs) and multi-modal LLMs have been remarkable.
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding
Temporal sentence grounding aims to detect event timestamps described by the natural language query from given untrimmed videos.
Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training
In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream tasks.
Branches, Assemble! Multi-Branch Cooperation Network for Large-Scale Click-Through Rate Prediction at Taobao
In this work, we introduce a novel Multi-Branch Cooperation Network (MBCnet) which enables multiple branch networks to collaborate with each other for better complex feature interaction modeling.
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
To address this, we propose a Counterfactual learning-driven representation disentanglement framework for search-enhanced recommendation, based on the common belief that a user would click an item under a query not solely because of the item-query match but also due to the item's query-independent general features (e. g., color or style) that interest the user.
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks
As the last stage of a typical \textit{recommendation system}, \textit{collective recommendation} aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance.
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models
Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance.
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos
To generate coherent motions, we first leverage the Kalman filter to construct smooth crops in the focus tunnel and inject the position embedding of the tunnel into attention layers to improve the continuity of the generated videos.
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.
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.
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.
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.
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.
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.
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.
Category-Oriented Representation Learning for Image to Multi-Modal Retrieval
We focus on representation learning for IMMR and analyze three key challenges for it: 1) skewed data and noisy label in real-world industrial data, 2) the information-inequality between image and text modality of documents when learning representations, 3) effective and efficient training in large-scale industrial contexts.
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.
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).
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$\%$.
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
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.
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.
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
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.
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.
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.
