Search Results for author:
Found 59 papers, 36 papers with code
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.
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.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
We study the hypothesis testing problem of inferring the existence of combinatorial structures in undirected graphical models.
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)?
PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution
We introduce PreCo, a large-scale English dataset for coreference resolution.
RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization
Joint replacement is the most common inpatient surgical treatment in the US.
Indices Matter: Learning to Index for Deep Image Matting
We show that existing upsampling operators can be unified with the notion of the index function.
Index Network
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
Ranked #2 on
Grayscale Image Denoising
on Set12 sigma30
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 #3 on
Crowd Counting
on TRANCOS
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.
The 1st Challenge on Remote Physiological Signal Sensing (RePSS)
Remote measurement of physiological signals from videos is an emerging topic.
A Learning-based Iterative Method for Solving Vehicle Routing Problems
This paper is concerned with solving combinatorial optimization problems, in particular, the capacitated vehicle routing problems (CVRP).
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.
The Effect of Dipole from $γ$-AgI Substrates on Heterogeneous Ice Nucleation
Heterogeneous ice nucleation is one of the most common and important process in the physical environment.
Materials Science
Learning Affinity-Aware Upsampling for Deep Image Matting
By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affinity-Aware Upsampling (A2U) where upsampling kernels are generated using a light-weight lowrank bilinear model and are conditioned on second-order features.
TransView: Inside, Outside, and Across the Cropping View Boundaries
We show that relation modeling between visual elements matters in cropping view recommendation.
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Bootstrapping provides a flexible and effective approach for assessing the quality of batch reinforcement learning, yet its theoretical property is less understood.
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement
Remote photoplethysmography (rPPG) based physiological measurement has great application values in health monitoring, emotion analysis, etc.
Composing Photos Like a Photographer
To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.
Ranked #1 on
Image Cropping
on FLMS
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.
Ranked #4 on
Object Counting
on CARPK
BokehMe: When Neural Rendering Meets Classical Rendering
Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.
3D Instances as 1D Kernels
The idea of instance kernel is inspired by recent success of dynamic convolutions in 2D/3D instance segmentation.
Ranked #2 on
3D Instance Segmentation
on S3DIS
(mCov metric)
MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects
Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background.
Robust Object Detection With Inaccurate Bounding Boxes
As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data.
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting.
Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
Generative adversarial networks (GANs) have been trained to be professional artists able to create stunning artworks such as face generation and image style transfer.
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields
To mitigate this issue, we introduce DoF-NeRF, a novel neural rendering approach that can deal with shallow DoF inputs and can simulate DoF effect.
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity.
Ranked #5 on
Feature Upsampling
on ImageNet
ParallelNet: Multi-mode Trajectory Prediction by Multi-mode Trajectory Fusion
Level 5 Autonomous Driving, a technology that a fully automated vehicle (AV) requires no human intervention, has raised serious concerns on safety and stability before widespread use.
Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting
Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet.
Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression
Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, ie, similar composition patterns.
Neuron Structure Modeling for Generalizable Remote Physiological Measurement
We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes.
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets
In this paper, we propose a principled approach that supports learning from heterogeneous datasets with different label sets.
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers.
Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair
Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras.
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset.
Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting
This task is typically addressed by extracting the features of query image and exemplars respectively and then matching their feature similarity, leading to an extract-then-match paradigm.
rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement
However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy.
A2B: Anchor to Barycentric Coordinate for Robust Correspondence
This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences.
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems.
Box-DETR: Understanding and Boxing Conditional Spatial Queries
In DAB-DETR, such queries are modulated by the so-called conditional linear projection at each decoder stage, aiming to search for positions of interest such as the four extremities of the box.
On Point Affiliation in Feature Upsampling
We introduce the notion of point affiliation into feature upsampling.
Fast Full-frame Video Stabilization with Iterative Optimization
Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one.
ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer
To this end, we introduce a new model named Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter), which achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder.
An interpretable deep learning method for bearing fault diagnosis
During the model evaluation process, the proposed approach retrieves prediction basis samples from the health library according to the similarity of the feature importance.
Point-Query Quadtree for Crowd Counting, Localization, and More
Such a querying process yields an intuitive, universal modeling of crowd as both the input and output are interpretable and steerable.
Learning to Upsample by Learning to Sample
We present DySample, an ultra-lightweight and effective dynamic upsampler.
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes.
Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement
In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities.
When Mining Electric Locomotives Meet Reinforcement Learning
In this paper, we present how to apply RL to the autonomous control of mining electric locomotives.
An Incremental Unified Framework for Small Defect Inspection
Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing.
Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation
In this study, we construct the largest preprocessed unlabeled TOF-MRA datasets (1510 subjects) to date.
Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates
This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV).
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge
Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.
