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Found 48 papers, 15 papers with code
Object Tracking using Spatio-Temporal Networks for Future Prediction Location
To facilitate the future prediction ability, we follow three key observations: 1) object motion trajectory is affected significantly by camera motion; 2) the past trajectory of an object can act as a salient cue to estimate the object motion in the spatial domain; 3) previous frames contain the surroundings and appearance of the target object, which is useful for predicting the target object’s future locations.
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT.
Intrinsic Bayesian Optimisation on Complex Constrained Domain
Motivated by the success of Bayesian optimisation algorithms in the Euclidean space, we propose a novel approach to construct Intrinsic Bayesian optimisation (In-BO) on manifolds with a primary focus on complex constrained domains or irregular-shaped spaces arising as submanifolds of R2, R3 and beyond.
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning
Learning effective motion features is an essential pursuit of video representation learning.
Modeling Global Distribution for Federated Learning with Label Distribution Skew
It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage.
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
Client Selection for Federated Bayesian Learning
Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server.
Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images.
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging.
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
Evaluating and Mitigating Static Bias of Action Representations in the Background and the Foreground
Deep neural networks for video action recognition easily learn to utilize shortcut static features, such as background and objects instead of motion features.
General non-linear imitation leads to limit cycles in eco-evolutionary dynamics
We extrapolate from the linear imitation function to general imitation function, which can be non-linear.
Few-Shot Learning of Accurate Folding Landscape for 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 therapeutical development.
Detecting and Preventing Shortcut Learning for Fair Medical AI using Shortcut Testing (ShorT)
Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities.
Progressively-connected Light Field Network for Efficient View Synthesis
ProLiF encodes a 4D light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses.
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.
Robust and Efficient Medical Imaging with Self-Supervision
These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
False Data Injection Attack on Electric Vehicle-Assisted Voltage Regulation
With the large scale penetration of electric vehicles (EVs) and the advent of bidirectional chargers, EV aggregators will become a major player in the voltage regulation market.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving.
Harmonic Modeling, Data Generation, and Analysis of Power Electronics-Interfaced Residential Loads
Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization.
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds.
Improving 3D Object Detection with Channel-wise Transformer
Though 3D object detection from point clouds has achieved rapid progress in recent years, the lack of flexible and high-performance proposal refinement remains a great hurdle for existing state-of-the-art two-stage detectors.
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Existing works use a network to learn point-wise weights for weighted least squares surface fitting to estimate the normals, which has difficulty in finding accurate normals in complex regions or containing noisy points.
Ranked #4 on
Surface Normals Estimation
on PCPNet
A Contract Theory based Incentive Mechanism for Federated Learning
It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.
Neural Rays for Occlusion-aware Image-based Rendering
On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction.
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.
The Blessings of Unlabeled Background in Untrimmed Videos
The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Noise Is Useful: Exploiting Data Diversity for Edge Intelligence
Edge intelligence requires to fast access distributed data samples generated by edge devices.
Information Theory Information Theory
Supervised Transfer Learning at Scale for Medical Imaging
However, for medical imaging, the value of transfer learning is less clear.
Contrast and Order Representations for Video Self-Supervised Learning
To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.
Point2Skeleton: Learning Skeletal Representations from Point Clouds
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds.
Learnable Motion Coherence for Correspondence Pruning
We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian.
Object Tracking Using Spatio-Temporal Future Prediction
Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory.
Addressing the Real-world Class Imbalance Problem in Dermatology
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes.
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability.
FedCoin: A Peer-to-Peer Payment System for Federated Learning
In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol.
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations.
A deep learning system for differential diagnosis of skin diseases
In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).
AI for Earth: Rainforest Conservation by Acoustic Surveillance
Saving rainforests is a key to halting adverse climate changes.
Multi-granularity Generator for Temporal Action Proposal
In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information.
Ranked #2 on
Action Recognition
on THUMOS’14
An Attention-Based Approach for Single Image Super Resolution
We propose an attention-based approach to give a discrimination between texture areas and smooth areas.
Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos
This note describes the details of our solution to the dense-captioning events in videos task of ActivityNet Challenge 2018.
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire
Sparse representation learning has recently gained a great success in signal and image processing, thanks to recent advances in dictionary learning.
