Search Results for author:
Found 33 papers, 8 papers with code
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Display advertising provides significant value to advertisers, publishers, and users.
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
Modeling feature interactions plays a crucial role in accurately predicting click-through rates (CTR) in advertising systems.
Rethinking Domain Adaptation and Generalization in the Era of CLIP
In recent studies on domain adaptation, significant emphasis has been placed on the advancement of learning shared knowledge from a source domain to a target domain.
Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
However, this leads to a challenging dilemma in MDL.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability.
Ads Recommendation in a Collapsed and Entangled World
We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations.
ReplaceAnything3D:Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields
We introduce ReplaceAnything3D model (RAM3D), a novel text-guided 3D scene editing method that enables the replacement of specific objects within a scene.
TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion
In contrast, TextureDreamer can transfer highly detailed, intricate textures from real-world environments to arbitrary objects with only a few casually captured images, potentially significantly democratizing texture creation.
Pareto-based Multi-Objective Recommender System with Forgetting Curve
Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback.
ESGReveal: An LLM-based approach for extracting structured data from ESG reports
ESGReveal is an innovative method proposed for efficiently extracting and analyzing Environmental, Social, and Governance (ESG) data from corporate reports, catering to the critical need for reliable ESG information retrieval.
GauFRe: Gaussian Deformation Fields for Real-time Dynamic Novel View Synthesis
We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video.
Tiled Multiplane Images for Practical 3D Photography
Based on the observation that the depth complexity in local image regions is lower than that over the entire image, we split an MPI into many small, tiled regions, each with only a few depth planes.
AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models
The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle.
AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow.
Temporally Consistent Online Depth Estimation Using Point-Based Fusion
The presence of dynamic objects further complicates the problem.
Multimodal High-order Relation Transformer for Scene Boundary Detection
Scene boundary detection breaks down long videos into meaningful story-telling units and plays a crucial role in high-level video understanding.
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
SNeRF: Stylized Neural Implicit Representations for 3D Scenes
This paper presents a stylized novel view synthesis method.
NeuralPassthrough: Learned Real-Time View Synthesis for VR
Virtual reality (VR) headsets provide an immersive, stereoscopic visual experience, but at the cost of blocking users from directly observing their physical environment.
View Synthesis of Dynamic Scenes based on Deep 3D Mask Volume
Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
LiveView: Dynamic Target-Centered MPI for View Synthesis
Existing Multi-Plane Image (MPI) based view-synthesis methods generate an MPI aligned with the input view using a fixed number of planes in one forward pass.
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn user preferences on various actions and topics unitedly.
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question.
Optical Character Recognition
Optical Character Recognition (OCR)
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KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs
Comparative results demonstrate that the proposed graph clustering algorithm is accurate yet efficient for large networks, which also means that it can be further used to evaluate the intra-cluster and inter-cluster trusts on large networks.
A feature-supervised generative adversarial network for environmental monitoring during hazy days
First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps.
Neural supersampling for real-time rendering
Due to higher resolutions and refresh rates, as well as more photorealistic effects, real-time rendering has become increasingly challenging for video games and emerging virtual reality headsets.
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolutional neural networks and recurrent neural networks).
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Then we extend the model family to a variety of bayesian online models with increasing feature embedding capabilities, such as Sparse-MLP, FM-MLP and FFM-MLP.
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency.
Defocus Deblurring and Superresolution for Time-of-Flight Depth Cameras
Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications.
Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors
The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light.
