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Found 32 papers, 16 papers with code
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
Interior Attention-Aware Network for Infrared Small Target Detection
Motivated by the fact that pixels from targets or backgrounds are correlated to each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection.
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.
Composing Photos Like a Photographer
To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
TransView: Inside, Outside, and Across the Cropping View Boundaries
We show that relation modeling between visual elements matters in cropping view recommendation.
On Efficient and Robust Metrics for RANSAC Hypotheses and 3D Rigid Registration
This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration.
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
3D Correspondence Grouping with Compatibility Features
We present a simple yet effective method for 3D correspondence grouping.
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning
Inspired by scale weighing, we propose a novel 'counting scale' termed LibraNet where the count value is analogized by weight.
ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification
Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
Specifically, we first sample seeds from the point clouds in template and search area respectively.
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching.
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global Topology
To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis.
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed.
Ranked #1 on
Depth Estimation
on NYU-Depth V2
(mAP metric)
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
A dense region can always be divided until sub-region counts are within the previously observed closed set.
Ranked #4 on
Crowd Counting
on ShanghaiTech A
Comparative evaluation of 2D feature correspondence selection algorithms
Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks.
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences
Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision.
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc.
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification
Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc.
Deep attention-based classification network for robust depth prediction
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
Action Recognition for Depth Video using Multi-view Dynamic Images
To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed.
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
Performance Evaluation of 3D Correspondence Grouping Algorithms
This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.
When Unsupervised Domain Adaptation Meets Tensor Representations
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.
TasselNet: Counting maize tassels in the wild via local counts regression network
To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.
