Search Results for author:
Found 46 papers, 18 papers with code
PromptPose: Language Prompt Helps Animal Pose Estimation
In PromptPose, we propose that adapting the language knowledge to the visual animal poses is key to achieve effective animal pose estimation.
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame.
Vision Transformer Adapter for Dense Predictions
When fine-tuning on downstream tasks, a modality-specific adapter is used to introduce the data and tasks' prior information into the model, making it suitable for these tasks.
Ranked #1 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Contrastive Boundary Learning for Point Cloud Segmentation
Point cloud segmentation is fundamental in understanding 3D environments.
Ranked #4 on
Semantic Segmentation
on S3DIS Area5
Load-Flow Solvability under Security Constraints in DC Distribution Networks
We present sufficient conditions for the load-flow solvability under security constraints in DC distribution networks.
Low-rank features based double transformation matrices learning for image classification
However, methods based on this technique ignore the pressure on a single transformation matrix due to the complex information contained in the data.
SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection
We observe that the prevailing set abstraction design for down-sampling points may maintain too much unimportant background information that can affect feature learning for detecting objects.
Recurrent Glimpse-based Decoder for Detection with Transformer
Then, a glimpse-based decoder is introduced to provide refined detection results based on both the glimpse features and the attention modeling outputs of the previous stage.
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation
Whereas adversarial training can be useful against specific adversarial perturbations, they have also proven ineffective in generalizing towards attacks deviating from those used for training.
MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar
Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies.
FAST: Searching for a Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation
We propose an accurate and efficient scene text detection framework, termed FAST (i. e., faster arbitrarily-shaped text detector).
Ranked #2 on
Scene Text Detection
on SCUT-CTW1500
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition
Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications.
Enhancing RF Sensing with Deep Learning: A Layered Approach
To better understand this potential, this article takes a layered approach to summarize RF sensing enabled by deep learning.
V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern.
SiWa: See into Walls via Deep UWB Radar
To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data.
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Dropout has been commonly used to quantify prediction uncertainty, i. e, the variations of model predictions on a given input example.
Variational Component Decoder for Source Extraction from Nonlinear Mixture
In many practical scenarios of signal extraction from a nonlinear mixture, only one (signal) source is intended to be extracted.
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.
Ranked #1 on
Object Tracking
on VisEvent
Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking
In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy.
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
(1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution.
Symmetry Breaking for k-Robust Multi-Agent Path Finding
During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events.
Recent Progress in Appearance-based Action Recognition
Action recognition, which is formulated as a task to identify various human actions in a video, has attracted increasing interest from computer vision researchers due to its importance in various applications.
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Invertible Neural BRDF for Object Inverse Rendering
We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i. e., joint estimation of reflectance and natural illumination from a single image of an object of known geometry.
Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory
This method can act as a plug-in for Fast Style Transfer without any modification to the network architecture.
Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.
Condensing Two-stage Detection with Automatic Object Key Part Discovery
Modern two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy.
Twisting operators and centralisers of Lie type groups over local rings
We extend the classical result asserting that the twisting operator preserves certain Deligne--Lusztig character values for truncated formal power series; along the way we discuss some properties of centralisers.
Representation Theory
Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning
This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.
TextFuseNet: Scene Text Detection with Richer Fused Features
More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
Towards High Performance Human Keypoint Detection
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance.
Ranked #5 on
Pose Estimation
on COCO test-dev
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Alternatively, to access much more natural-looking pedestrians, we propose to augment pedestrian detection datasets by transforming real pedestrians from the same dataset into different shapes.
Human Keypoint Detection by Progressive Context Refinement
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance of person instances.
Transition Subspace Learning based Least Squares Regression for Image Classification
Only learning one projection matrix from original samples to the corresponding binary labels is too strict and will consequentlly lose some intrinsic geometric structures of data.
Progressive LiDAR Adaptation for Road Detection
To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises.
Low-Rank Discriminative Least Squares Regression for Image Classification
To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification.
Fisher Discriminative Least Squares Regression for Image Classification
On one hand, the Fisher criterion improves the intra-class compactness of the relaxed labels during relaxation learning.
Non-negative representation based discriminative dictionary learning for face recognition
In this paper, we propose a non-negative representation based discriminative dictionary learning algorithm (NRDL) for multicategory face classification.
Context Refinement for Object Detection
Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions.
An Experimental Survey on Correlation Filter-based Tracking
We find that further improvements for correlation filter-based tracking can be made on estimating scales, applying part-based tracking strategy and cooperating with long-term tracking methods.
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking.
Nonparametric Estimation of Band-limited Probability Density Functions
In this paper, a nonparametric maximum likelihood (ML) estimator for band-limited (BL) probability density functions (pdfs) is proposed.
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Rodent hippocampal population codes represent important spatial information about the environment during navigation.
