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Found 52 papers, 26 papers with code
Panoptic Video Scene Graph Generation
PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem
Full-waveform inversion (FWI) is a powerful geophysical imaging technique that infers high-resolution subsurface physical parameters by solving a non-convex optimization problem.
Bounding and Filling: A Fast and Flexible Framework for Image Captioning
Several models adopted a non-autoregressive manner to speed up the process.
Dynamic Demonstrations Controller for In-Context Learning
Previous studies have revealed that ICL is sensitive to the selection and the ordering of demonstrations.
Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models
Experiments on our proposed datasets demonstrate that popular VLMs underperform in the food domain compared with their performance in the general domain.
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training Model
Considering that Vision-Language Pre-Training (VLP) models master massive such knowledge from large-scale web-harvested data, it is promising to utilize the generalizability of VLP models to incorporate knowledge into image descriptions.
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora
Generating visually grounded image captions with specific linguistic styles using unpaired stylistic corpora is a challenging task, especially since we expect stylized captions with a wide variety of stylistic patterns.
Capturing the Diffusive Behavior of the Multiscale Linear Transport Equations by Asymptotic-Preserving Convolutional DeepONets
In this paper, we introduce two types of novel Asymptotic-Preserving Convolutional Deep Operator Networks (APCONs) designed to address the multiscale time-dependent linear transport problem.
Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion
To address this issue, this paper proposes an extension to PINNs called Laplace-based fractional physics-informed neural networks (Laplace-fPINNs), which can effectively solve the forward and inverse problems of fractional diffusion equations.
Distribution Fitting for Combating Mode Collapse in GANs
To alleviate the problem, we propose a global distribution fitting (GDF) method by a penalty term to constrain generated data distribution.
Bayesian Inversion with Neural Operator (BINO) for Modeling Subdiffusion: Forward and Inverse Problems
A large number of numerical experiments demonstrate that the operator learning method proposed in this work can efficiently solve the forward problems and Bayesian inverse problems of the subdiffusion equation.
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective
In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
GEDI: A Graph-based End-to-end Data Imputation Framework
Data imputation is an effective way to handle missing data, which is common in practical applications.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods.
Unified Chinese License Plate Detection and Recognition with High Efficiency
With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline.
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs
In this paper, we propose a a machine learning approach via model-operator-data network (MOD-Net) for solving PDEs.
An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network
frequency in DNN training.
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning
It draws class-wise features closer than coarse feature alignment or class-wise feature alignment only, therefore improves the model's performance to a great extent.
Network Pruning via Resource Reallocation
Channel pruning is broadly recognized as an effective approach to obtain a small compact model through eliminating unimportant channels from a large cumbersome network.
Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Why heavily parameterized neural networks (NNs) do not overfit the data is an important long standing open question.
Fourier-domain Variational Formulation and Its Well-posedness for Supervised Learning
A supervised learning problem is to find a function in a hypothesis function space given values on isolated data points.
On the exact computation of linear frequency principle dynamics and its generalization
Recent works show an intriguing phenomenon of Frequency Principle (F-Principle) that deep neural networks (DNNs) fit the target function from low to high frequency during the training, which provides insight into the training and generalization behavior of DNNs in complex tasks.
Fully Decentralized Federated Learning Based Beamforming Design for UAV Communications
To handle the data explosion in the era of internet of things (IoT), it is of interest to investigate the decentralized network, with the aim at relaxing the burden to central server along with keeping data privacy.
Signal Processing
Phase diagram for two-layer ReLU neural networks at infinite-width limit
In this work, inspired by the phase diagram in statistical mechanics, we draw the phase diagram for the two-layer ReLU neural network at the infinite-width limit for a complete characterization of its dynamical regimes and their dependence on hyperparameters related to initialization.
Path Integral Based Convolution and Pooling for Graph Neural Networks
Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs.
Inter-Region Affinity Distillation for Road Marking Segmentation
We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation.
Ranked #1 on
Semantic Segmentation
on ApolloScape
What's the relationship between CNNs and communication systems?
The interpretability of Convolutional Neural Networks (CNNs) is an important topic in the field of computer vision.
Defending from adversarial examples with a two-stream architecture
We demonstrate that our two-stream architecture is robust to adversarial examples built by currently known attacking algorithms.
Haar Graph Pooling
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.
HaarPooling: Graph Pooling with Compressive Haar Basis
The input of each pooling layer is transformed by the compressive Haar basis of the corresponding clustering.
STELA: A Real-Time Scene Text Detector with Learned Anchor
To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in scene text detection tasks.
Focus-Enhanced Scene Text Recognition with Deformable Convolutions
Recently, scene text recognition methods based on deep learning have sprung up in computer vision area.
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.
Ranked #6 on
Lane Detection
on BDD100K val
Fast Haar Transforms for Graph Neural Networks
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data.
Theory of the Frequency Principle for General Deep Neural Networks
Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training.
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
It remains a puzzle that why deep neural networks (DNNs), with more parameters than samples, often generalize well.
A type of generalization error induced by initialization in deep neural networks
Overall, our work serves as a baseline for the further investigation of the impact of initialization and loss function on the generalization of DNNs, which can potentially guide and improve the training of DNNs in practice.
PAN: Path Integral Based Convolution for Deep Graph Neural Networks
In this paper, we propose PAN, a new graph convolution framework that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk.
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #7 on
Face Alignment
on AFLW2000-3D
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing
Since sparse unmixing has emerged as a promising approach to hyperspectral unmixing, some spatial-contextual information in the hyperspectral images has been exploited to improve the performance of the unmixing recently.
Generating Text Sequence Images for Recognition
Recently, methods based on deep learning have dominated the field of text recognition.
Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.
Deep Transfer Across Domains for Face Anti-spoofing
We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing.
Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
Spatio-temporal information is very important to capture the discriminative cues between genuine and fake faces from video sequences.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.
Ranked #2 on
Face Anti-Spoofing
on MSU-MFSD
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Reinforcement learning agents need exploratory behaviors to escape from local optima.
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction.
Ranked #1 on
Steering Control
on BDD100K val
Detecting Multi-Oriented Text with Corner-based Region Proposals
Previous approaches for scene text detection usually rely on manually defined sliding windows.
Ranked #1 on
Scene Text Detection
on COCO-Text
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
The goal of this paper is to evaluate density maps generated by density estimation methods on a variety of crowd analysis tasks, including counting, detection, and tracking.
Small Instance Detection by Integer Programming on Object Density Maps
For each region, a sliding window (ROI) is passed over the density map to calculate the instance count within each ROI.
Crossing the Line: Crowd Counting by Integer Programming with Local Features
Next, the number of people is estimated in a set of overlapping sliding windows on the temporal slice image, using a regression function that maps from local features to a count.
