Search Results for author:
Found 24 papers, 17 papers with code
BEV-MAE: Bird's Eye View Masked Autoencoders for Outdoor Point Cloud Pre-training
Besides, we introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder with fine-tuning for masked point cloud inputs.
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting.
Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with Transformers
Firstly, we present a foreground guidance strategy with an off-the-shelf UOD detector to highlight the foreground regions on the feature maps and then refine object locations in an iterative fashion.
Differentiable Architecture Search with Random Features
Differentiable architecture search (DARTS) has significantly promoted the development of NAS techniques because of its high search efficiency and effectiveness but suffers from performance collapse.
Ranked #11 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
FlowNAS: Neural Architecture Search for Optical Flow Estimation
To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR.
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks.
IterVM: Iterative Vision Modeling Module for Scene Text Recognition
By combining the proposed IterVM with iterative language modeling module, we further propose a powerful scene text recognizer called IterNet.
Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching
The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models.
Continual Contrastive Learning for Image Classification
In this paper, we make the first attempt to tackle the catastrophic forgetting problem in the mainstream self-supervised methods, i. e., contrastive learning methods.
CBNet: A Composite Backbone Network Architecture for Object Detection
With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
Ranked #11 on
Instance Segmentation
on COCO test-dev
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
RPATTACK: Refined Patch Attack on General Object Detectors
Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually.
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy.
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance.
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation.
DADA: Differentiable Automatic Data Augmentation
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.
Ranked #11 on
Data Augmentation
on ImageNet
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.
MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection
Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection.
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #38 on
Instance Segmentation
on COCO test-dev
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #146 on
Object Detection
on COCO test-dev
Deep Dual Pyramid Network for Barcode Segmentation using Barcode-30k Database
Specifically, a synthesizing method was proposed to generate well-annotated images containing barcode and QR code labels, which contributes to largely decrease the annotation time.
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD).
Mutual Enhancement for Detection of Multiple Logos in Sports Videos
Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames.
