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Found 38 papers, 12 papers with code
ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
A self-attention-based differentially private tabular GAN with high data utility
Generative Adversarial Networks (GANs) have become a ubiquitous technology for data generation, with their prowess in image generation being well-established.
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.
Towards Fair and Comprehensive Comparisons for Image-Based 3D Object Detection
In this work, we build a modular-designed codebase, formulate strong training recipes, design an error diagnosis toolbox, and discuss current methods for image-based 3D object detection.
Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator
PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios.
Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics
The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage.
TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers
The Vertex-based Merging Module is capable of aggregating local contextual information between adjacent basic grids, providing the ability to merge basic girds that belong to the same spanning cell accurately.
Ranked #5 on
Table Recognition
on PubTabNet
Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation
Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation.
Fine-grained Retrieval Prompt Tuning
In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompting and feature adaptation.
MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
Finally, this LiDAR Net can serve as the teacher to transfer the learned knowledge to the baseline model.
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset
Based on this dataset, we propose a semi-supervised underwater semantic segmentation network focusing on the boundaries(US-Net: Underwater Segmentation Network).
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection
In this paper, we propose to learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection.
Ranked #10 on
Monocular 3D Object Detection
on KITTI Cars Moderate
2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
Ranked #1 on
Object Detection
on Waymo Open Dataset
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
A Dataset And Benchmark Of Underwater Object Detection For Robot Picking
Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets.
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution
First, we design a cross-task distillation scheme that encourages DSR and DE networks to learn from each other in a teacher-student role-exchanging fashion.
Quality-Aware Network for Human Parsing
How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing.
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one.
Direct Depth Learning Network for Stereo Matching
To make the Adaptive-Grained Depth Refinement stage robust to the coarse depth and adaptive to the depth range of the points, the Granularity Uncertainty is introduced to Adaptive-Grained Depth Refinement stage.
Full Matching on Low Resolution for Disparity Estimation
To this end, we first propose to decompose the full matching task into multiple stages of the cost aggregation module.
Character randomized benchmarking for non-multiplicity-free groups with applications to subspace, leakage, and matchgate randomized benchmarking
Finally, we derive a scalable RB protocol for the matchgate group, a group that like the Clifford group is non-universal but becomes universal with the addition of one additional gate.
Quantum Physics
Category-specific Semantic Coherency Learning for Fine-grained Image Recognition
Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature (HLF) maps directly.
Renovating Parsing R-CNN for Accurate Multiple Human Parsing
Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously.
Rethink Maximum Mean Discrepancy for Domain Adaptation
On the other hand, we design two different strategies to boost the feature discriminability: 1) we directly impose a trade-off parameter on the implicit intra-class distance in MMD to regulate its change; 2) we impose the similar weights revealed in MMD on inter-class distance and maximize it, then a balanced factor could be introduced to quantitatively leverage the relative importance between the feature transferability and its discriminability.
Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature maps directly.
Ranked #14 on
Fine-Grained Image Classification
on FGVC Aircraft
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
Sparsely-Labeled Source Assisted Domain Adaptation
However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild.
Location-Aware Feature Selection Text Detection Network
However, they are behind in accuracy comparing with recent segmentation-based text detectors.
CPM R-CNN: Calibrating Point-guided Misalignment in Object Detection
In object detection, offset-guided and point-guided regression dominate anchor-based and anchor-free method separately.
A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing
We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively.
Planning for Compilation of a Quantum Algorithm for Graph Coloring
Previous work demonstrated that temporal planning is an attractive approach for part of this compilationtask, specifically, the routing of circuits that implement the Quantum Alternating Operator Ansatz (QAOA) applied to the MaxCut problem on a quantum processor architecture.
Graph-propagation based Correlation Learning for Weakly Supervised Fine-grained Image Classification
The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them.
Ranked #25 on
Fine-Grained Image Classification
on FGVC Aircraft
Importance Filtered Cross-Domain Adaptation
Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably.
Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving
To this end, we first leverage a stand-alone module to transform the input data from 2D image plane to 3D point clouds space for a better input representation, then we perform the 3D detection using PointNet backbone net to obtain objects 3D locations, dimensions and orientations.
Parsing R-CNN for Instance-Level Human Analysis
Models need to distinguish different human instances in the image panel and learn rich features to represent the details of each instance.
Ranked #1 on
Pose Estimation
on DensePose-COCO
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization.
A Single Shot Text Detector with Scale-adaptive Anchors
Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances.
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
The essence of this extension, the Quantum Alternating Operator Ansatz, is the consideration of general parametrized families of unitaries rather than only those corresponding to the time-evolution under a fixed local Hamiltonian for a time specified by the parameter.
Quantum Physics
