Search Results for author:
Found 35 papers, 17 papers with code
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Beyond capturing global information like other multi-modal models, our proposed model excels at tasks demanding a detailed understanding of local information within the input.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation
In this paper, we propose an adaptive weighting regression (AWR) method to leverage the advantages of both detection-based and regression-based methods.
Ranked #1 on
Hand Pose Estimation
on HANDS 2017
Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image
On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning.
Ranked #3 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding
Our framework introduces two auxiliary tasks, cross-modal matching and temporal order discrimination, to promote the grounding model training.
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
Many channel pruning works utilize structured sparsity regularization to zero out all the weights in some channels and automatically obtain structure-sparse network in training stage.
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
Compared with AUROC, AUPRC is a more appropriate metric for highly imbalanced datasets.
A Simple and Effective Framework for Pairwise Deep Metric Learning
To tackle this issue, we propose a simple and effective framework to sample pairs in a batch of data for updating the model.
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives
In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications in machine learning for improving the robustness of neural networks.
Attentional-Biased Stochastic Gradient Descent
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning.
A Variational U-Net for Weather Forecasting
Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change.
How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?
It shows that finetuning PLMs with diffusion degrades the reconstruction ability on OOD data.
Mining Multi-View Information: A Strong Self-Supervised Framework for Depth-Based 3D Hand Pose and Mesh Estimation
However, these methods ignore the rich semantic information in each view and ignore the complex dependencies between different regions of different views.
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding
However, the decoding process in EA - essential for effective operation and alignment accuracy - has received limited attention and remains tailored to specific datasets and model architectures, necessitating both entity and additional explicit relation embeddings.
Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
This study introduces a novel approach, DESAlign, which addresses these issues by applying a theoretical framework based on Dirichlet energy to ensure semantic consistency.
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models.
A Deep Tree-Structured Fusion Model for Single Image Deraining
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem.
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
In this paper, we propose new stochastic optimization algorithms and study their first-order convergence theories for solving a broad family of DC functions.
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification
It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification
Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context.
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation
As most weakly supervised object detection methods are based on pre-generated proposals, they often show two false detections: (i) group multiple object instances with one bounding box, and (ii) focus on only parts rather than the whole objects.
Ranked #25 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection
With the recent progress of deep learning, advanced industrial object detectors are built for smart industrial applications.
Ranked #22 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis
To tackle these problems, we design a post-training procedure, which contains the target domain masked language model task and a novel domain-distinguish pre-training task.
Variance-Reduced Off-Policy Memory-Efficient Policy Search
To achieve variance-reduced off-policy-stable policy optimization, we propose an algorithm family that is memory-efficient, stochastically variance-reduced, and capable of learning from off-policy samples.
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserve prices for revenue-maximizing auctioneers in both repeated auctions and uniform-price auctions.
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos.
Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
We also explored the use of Graph Neural Networks and introduced a novel ensemble GNN architecture which outperformed the GNN solution from last year.
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation
In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification.
SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception
In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions.
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance
Second, we propose Inverse Learning Guidance to improve the selection of aspect feature by considering aspect correlation, which provides more useful information to determine polarity.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Stochastic Constrained DRO with a Complexity Independent of Sample Size
Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention in recent years.
Fairness via Adversarial Attribute Neighbourhood Robust Learning
Improving fairness between privileged and less-privileged sensitive attribute groups (e. g, {race, gender}) has attracted lots of attention.
Improving Identity-Robustness for Face Models
When it comes to models directly trained on human faces, a sensitive confounder is that of human identities.
Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource
Deep Neural Networks (DNNs) have significantly improved the accuracy of intelligent applications on mobile devices.
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers.
