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Found 18 papers, 6 papers with code
Towards Stable 3D Object Detection
In autonomous driving, the temporal stability of 3D object detection greatly impacts the driving safety.
HAAP: Vision-context Hierarchical Attention Autoregressive with Adaptive Permutation for Scene Text Recognition
It reduces the training overhead of PLM while avoiding training fit oscillations.
Align-Free Multi-Plane Phase Retrieval
The multi-plane phase retrieval method provides a budget-friendly and effective way to perform phase imaging, yet it often encounters alignment challenges due to shifts along the optical axis in experiments.
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
Small, Versatile and Mighty: A Range-View Perception Framework
Our proposed Small, Versatile, and Mighty (SVM) network utilizes a pure convolutional architecture to fully unleash the efficiency and multi-tasking potentials of the range view representation.
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
We aim at providing the object detection community with an efficient and performant object detector, termed YOLO-MS.
CrossKD: Cross-Head Knowledge Distillation for Object Detection
Moreover, as mimicking the teacher's predictions is the target of KD, CrossKD offers more task-oriented information in contrast with feature imitation.
FewSAR: A Few-shot SAR Image Classification Benchmark
Motivated by this observation, we propose a novel few-shot SAR image classification benchmark (FewSAR) to address this issue.
Feature Chirality in Deep Learning Models
As deep learning applications extensively increase by leaps and bounds, their interpretability has become increasingly prominent.
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification
Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID).
DAGAM: A Domain Adversarial Graph Attention Model for Subject Independent EEG-Based Emotion Recognition
One of the most significant challenges of EEG-based emotion recognition is the cross-subject EEG variations, leading to poor performance and generalizability.
Anchor-free Oriented Proposal Generator for Object Detection
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
Oriented R-CNN for Object Detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
Ranked #11 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism
It consists of a dense residual network structure, an adaptive weight channel attention (AWCA) module, a patch second non-local (PSNL) module and a soft label generation method.
Grafted network for person re-identification
In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion.
A heterogeneous branch and multi-level classification network for person re-identification
A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block.
Ensemble Feature for Person Re-Identification
However, deep feature extracted by a single CNN network is not robust enough in testing stage.
