Search Results for author:
Found 64 papers, 25 papers with code
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion
Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.
Optimizing for the Shortest Path in Denoising Diffusion Model
In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error.
Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data
Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery.
Machine Learning Aided Modeling of Granular Materials: A Review
Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017.
P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task
Embodied Everyday Task is a popular task in the embodied AI community, requiring agents to make a sequence of actions based on natural language instructions and visual observations.
Robust Simultaneous Multislice MRI Reconstruction Using Deep Generative Priors
Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions.
SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval
Furthermore, existing RGB-based sign retrieval works suffer from the huge memory cost of dense visual data embedding in end-to-end training, and adopt offline RGB encoder instead, leading to suboptimal feature representation.
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment.
DTR: A Unified Deep Tensor Representation Framework for Multimedia Data Recovery
Especially, the deep latent generative module can faithfully generate the latent tensor as compared with shallow matrix factorization.
Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition
Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production.
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition
On one hand, since the semantics of sign language are expressed by the cooperation of fine-grained hands and coarse-grained trunks, we utilize both granularity information and encode them into latent spaces.
MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition
To this end, we propose a Motion-Aware masked autoencoder with Semantic Alignment (MASA) that integrates rich motion cues and global semantic information in a self-supervised learning paradigm for SLR.
Autoencoder-assisted Feature Ensemble Net for Incipient Faults
Deep learning has shown the great power in the field of fault detection.
Token-Efficient Leverage Learning in Large Language Models
With the same amount of task data, TELL leads in improving task performance compared to SFT.
GaussNav: Gaussian Splatting for Visual Navigation
In embodied vision, Instance ImageGoal Navigation (IIN) requires an agent to locate a specific object depicted in a goal image within an unexplored environment.
Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction
To address the above problem, we propose a novel motion-aware enhancement framework for dynamic scene reconstruction, which mines useful motion cues from optical flow to improve different paradigms of dynamic 3DGS.
DEMOS: Dynamic Environment Motion Synthesis in 3D Scenes via Local Spherical-BEV Perception
To handle this problem, we propose the first Dynamic Environment MOtion Synthesis framework (DEMOS) to predict future motion instantly according to the current scene, and use it to dynamically update the latent motion for final motion synthesis.
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval
To tackle the above problems, we propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning.
Structure Similarity Preservation Learning for Asymmetric Image Retrieval
The centroid vectors in the quantizer serve as anchor points in the embedding space of the gallery model to characterize its structure.
Asymmetric Feature Fusion for Image Retrieval
Then, a dynamic mixer is introduced to aggregate these features into compact embedding for efficient search.
Instance-aware Exploration-Verification-Exploitation for Instance ImageGoal Navigation
As a new embodied vision task, Instance ImageGoal Navigation (IIN) aims to navigate to a specified object depicted by a goal image in an unexplored environment.
Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach
It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models.
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.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
SDTracker: Synthetic Data Based Multi-Object Tracking
We present SDTracker, a method that harnesses the potential of synthetic data for multi-object tracking of real-world scenes in a domain generalization and semi-supervised fashion.
HandNeRF: Neural Radiance Fields for Animatable Interacting Hands
We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views.
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis
The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution.
FashionVQA: A Domain-Specific Visual Question Answering System
We see that using the same transformer for encoding the question and decoding the answer, as in language models, achieves maximum accuracy, showing that visual language models (VLMs) make the best visual question answering systems for our dataset.
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
Cost-Sensitive Active Learning for Incomplete Data
A reasonable machine learning scenario involves obtaining certain values and labels at cost on request.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Rethinking Efficient Lane Detection via Curve Modeling
This paper presents a novel parametric curve-based method for lane detection in RGB images.
Ranked #2 on
Lane Detection
on LLAMAS
Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
We introduce a novel attribute similarity metric to encourage networks to learn the style of facial attributes from the exemplar in a self-supervised way.
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness
Extensive experiments on well-known white- and black-box attacks show that MFDV-SNN achieves a significant improvement over existing methods, which indicates that it is a simple but effective method to improve model robustness.
Contextual Similarity Distillation for Asymmetric Image Retrieval
To this end, we propose a flexible contextual similarity distillation framework to enhance the small query model and keep its output feature compatible with that of large gallery model, which is crucial with asymmetric retrieval.
Learning Token-based Representation for Image Retrieval
Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.
Ranked #3 on
Image Retrieval
on RParis (Medium)
Exploring Versatile Prior for Human Motion via Motion Frequency Guidance
Prior plays an important role in providing the plausible constraint on human motion.
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking
Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature
For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.
THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation
It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT
In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations.
Learning Deep Local Features With Multiple Dynamic Attentions for Large-Scale Image Retrieval
To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image.
AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy
Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).
Eigenvalue-corrected Natural Gradient Based on a New Approximation
Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers.
Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography
The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image.
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
There have been many attempts to use second-order optimization methods for training deep neural networks.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
In this paper, we introduce body part segmentation as critical supervision.
Ranked #104 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Empirical distributions of the robustified $t$-test statistics
Based on the median and the median absolute deviation estimators, and the Hodges-Lehmann and Shamos estimators, robustified analogues of the conventional $t$-test statistic are proposed.
Applications
Weight-importance sparse training in keyword spotting
Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS).
Deep Multiscale Model Learning
Numerical results show that using deep learning and multiscale models, we can improve the forward models, which are conditioned to the available data.
Yuan at SemEval-2018 Task 1: Tweets Emotion Intensity Prediction using Ensemble Recurrent Neural Network
We perform the LSTM and BiLSTM model for the emotion intensity prediction.
DRPose3D: Depth Ranking in 3D Human Pose Estimation
In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation.
YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.
Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure
A topological subdivisioning is adopted to reduce the connection between the input channels and output channels.
Sparse Convolutional Neural Networks
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.
