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Found 29 papers, 16 papers with code
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
In real-world scenarios, images captured often suffer from blurring, noise, and other forms of image degradation, and due to sensor limitations, people usually can only obtain low dynamic range images.
Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules.
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach
Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
Towards High-quality HDR Deghosting with Conditional Diffusion Models
To address this challenge, we formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition, consisting of the feature condition generator and the noise predictor.
Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area.
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids
To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors.
Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt
To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained ConvNeXt model.
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it.
Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment
Considering the excessive memory overheads of contrastive learning, we further propose a negative-free solution, where the main contribution is a Graph Signal Decorrelation (GSD) constraint to avoid representation collapse and over-smoothing.
RISE-Based Adaptive Control with Mass-Inertia Parameter Estimation for Aerial Transportation of Multi-Rotor UAVs
Then, adaptive estimation laws for mass-inertia parameters are designed based on a filter operation.
Differentially Private Covariance Revisited
In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP).
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization
In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing.
Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective
We further collect a dataset of indoor and outdoor LiDAR scenes in the posed range image format.
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
In this paper, we propose an approach to bridge pre-trained models and code-related tasks.
ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception
Unlike existing GPU hash maps, the ASH framework provides a versatile tensor interface, hiding low-level details from the users.
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language.
Self-supervised Geometric Perception
We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e. g., camera poses, rigid transformations).
Compositional Scalable Object SLAM
We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects.
Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs
In the disease prediction task, our model achieves 0. 743 and 0. 639 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the two datasets, respectively.
Deep Global Registration
We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans.
Ranked #2 on
Point Cloud Registration
on KITTI (FCGF setting)
Deep representation learning for individualized treatment effect estimation using electronic health records
We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation.
Ranked #3 on
Causal Inference
on IHDP
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage.
Document Hashing with Mixture-Prior Generative Models
Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes.
Lung cancer screening with low-dose CT scans using a deep learning approach
We applied to this cohort our newly developed algorithm, DeepScreener, which is based on a novel deep learning approach.
Guided Feature Selection for Deep Visual Odometry
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks.
Boosting the Robustness Verification of DNN by Identifying the Achilles's Heel
How to ensure the safety of DNN-based system is a critical problem for the research and application of DNN.
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
We propose a novel 3D spatial representation for data fusion and scene reconstruction.
