Search Results for author:
Found 40 papers, 23 papers with code
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks
We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training.
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition
State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks.
Ranked #1 on
Action Classification
on Jester test
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM
To address these issues, we introduce EIVEN, a data- and parameter-efficient generative framework that pioneers the use of multimodal LLM for implicit attribute value extraction.
Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction
Scene reconstruction from multi-view images is a fundamental problem in computer vision and graphics.
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO.
Generative Human Motion Stylization in Latent Space
Building upon this, we present a novel generative model that produces diverse stylization results of a single motion (latent) code.
GO-NeRF: Generating Virtual Objects in Neural Radiance Fields
To this end, we propose a new method, GO-NeRF, capable of utilizing scene context for high-quality and harmonious 3D object generation within an existing NeRF.
Random resistive memory-based deep extreme point learning machine for unified visual processing
Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks.
Texture Generation on 3D Meshes with Point-UV Diffusion
In this work, we focus on synthesizing high-quality textures on 3D meshes.
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur
Rendering novel view images is highly desirable for many applications.
DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation
The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
Learning a Room with the Occ-SDF Hybrid: Signed Distance Function Mingled with Occupancy Aids Scene Representation
We found that the color rendering loss results in optimization bias against low-intensity areas, causing gradient vanishing and leaving these areas unoptimized.
Multi-Behavior Graph Neural Networks for Recommender System
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
HiVLP: Hierarchical Interactive Video-Language Pre-Training
However, compared to image-language pre-training, VLP has lagged far behind due to the lack of large amounts of video-text pairs.
CLIPPING: Distilling CLIP-Based Models With a Student Base for Video-Language Retrieval
Pre-training a vison-language model and then fine-tuning it on downstream tasks have become a popular paradigm.
ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i. e., ultra-high-definition) images.
Ranked #1 on
Image Restoration
on UHDM
Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking
The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance.
Video Demoireing with Relation-Based Temporal Consistency
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
An Efficient and Robust System for Vertically Federated Random Forest
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
Decompose the Sounds and Pixels, Recompose the Events
In this paper, we propose a framework centering around a novel architecture called the Event Decomposition Recomposition Network (EDRNet) to tackle the Audio-Visual Event (AVE) localization problem in the supervised and weakly supervised settings.
Self-supervised Spatiotemporal Representation Learning by Exploiting Video Continuity
Recent self-supervised video representation learning methods have found significant success by exploring essential properties of videos, e. g. speed, temporal order, etc.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
Social Recommendation with Self-Supervised Metagraph Informax Network
To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.
Graph Meta Network for Multi-Behavior Recommendation
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.
Class Semantics-based Attention for Action Detection
Our attention mechanism outperforms prior self-attention modules such as the squeeze-and-excitation in action detection task.
Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder
State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets.
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.
Learning a Proposal Classifier for Multiple Object Tracking
In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph.
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious.
Neural Point Cloud Rendering via Multi-Plane Projection
The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.
Heterogeneous Deep Graph Infomax
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
An Adaptive Psychoacoustic Model for Automatic Speech Recognition
Experiments are carried out on the AURORA2 database and show that the word recognition rate using our proposed feature extraction method is significantly increased over the baseline.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Topological Value Iteration Algorithms
Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs) because it puts the majority of its effort into backing up the entire state space, which turns out to be unnecessary in many cases.
