Search Results for author:
Found 38 papers, 13 papers with code
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
Ranked #8 on
Scene Text Detection
on SCUT-CTW1500
Segmenting Transparent Object in the Wild with Transformer
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Ranked #3 on
Semantic Segmentation
on Trans10K
Deep Learning for Multivariate Time Series Imputation: A Survey
In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods.
Scene Text Image Super-Resolution in the Wild
For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN.
Segmenting Transparent Objects in the Wild
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Ranked #4 on
Semantic Segmentation
on Trans10K
TextSR: Content-Aware Text Super-Resolution Guided by Recognition
Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.
Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction
As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close to the human body.
Ranked #6 on
3D Human Pose Estimation
on 3DPW
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
Elucidating The Design Space of Classifier-Guided Diffusion Generation
Guidance in conditional diffusion generation is of great importance for sample quality and controllability.
Ranked #1 on
Conditional Image Generation
on ImageNet 128x128
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement Learning
Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points.
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.
Uncertainty Quantification for Bayesian Optimization
Bayesian optimization is a class of global optimization techniques.
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data.
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process.
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.
Differentiable and Scalable Generative Adversarial Models for Data Imputation
DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions
Nested simulation concerns estimating functionals of a conditional expectation via simulation.
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data.
Enhancing Recommender Systems: A Strategy to Mitigate False Negative Impact
In implicit collaborative filtering (CF) task of recommender systems, recent works mainly focus on model structure design with promising techniques like graph neural networks (GNNs).
An automated approach to extracting positive and negative clinical research results
Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken.
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning
In this work, we systematically investigate the role of the projection head in SSL.
Random Smoothing Regularization in Kernel Gradient Descent Learning
In this paper, we aim to bridge this gap by presenting a framework for random smoothing regularization that can adaptively and effectively learn a wide range of ground truth functions belonging to the classical Sobolev spaces.
Learning Dense UV Completion for Human Mesh Recovery
Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module.
Data Upcycling Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models.
Ranked #22 on
Image Super-Resolution
on Urban100 - 4x upscaling
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis
Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e. g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism.
Regret Optimality of GP-UCB
We establish new upper bounds on both the simple and cumulative regret of GP-UCB when the objective function to optimize admits certain smoothness property.
EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation
We introduce Efficient Motion Diffusion Model (EMDM) for fast and high-quality human motion generation.
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling
Surprisingly, the improvement persists when we increase the number of sampling steps and can even surpass the best result from EDM-2 (1. 58) with only 39 NFEs (1. 57).
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
In this work, we aim to address this alignment challenge for conditional generation tasks.
3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models
In this work, we show that synthetic data created by generative models is complementary to computer graphics (CG) rendered data for achieving remarkable generalization performance on diverse real-world scenes for 3D human pose and shape estimation (HPS).
AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation
Expressive human pose and shape estimation (a. k. a.
Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning.
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model.
