Search Results for author:
Found 33 papers, 23 papers with code
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 #47 on
Instance Segmentation
on COCO test-dev
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 #6 on
Object Detection
on COCO-O
(using extra training data)
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.
DADA: Differentiable Automatic Data Augmentation
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.
Ranked #15 on
Data Augmentation
on ImageNet
DynamicDet: A Unified Dynamic Architecture for Object Detection
We also present a novel optimization strategy with an exiting criterion based on the detection losses for our dynamic detectors.
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes.
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.
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.
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.
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 #145 on
Object Detection
on COCO test-dev
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.
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.
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.
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Based on the property of outdoor point clouds in autonomous driving scenarios, i. e., the point clouds of distant objects are more sparse, we propose point density prediction to enable the 3D encoder to learn location information, which is essential for object detection.
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
In the dual-stream radar backbone, a point-based encoder and a transformer-based encoder are proposed to extract radar features, with an injection and extraction module to facilitate communication between the two encoders.
Ranked #2 on
3D Object Detection
on nuscenes Camera-Radar
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.
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).
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.
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.
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-view (BEV) semantic segmentation.
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.
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.
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.
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.
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.
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.
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.
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 #12 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation
Specifically, we propose a learnable nonlinear channel-wise transformation to align the features of the student and the teacher model.
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization
Stable Diffusion (SD) customization approaches enable users to personalize SD model outputs, greatly enhancing the flexibility and diversity of AI art.
SAMPLING: Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image
Recent novel view synthesis methods obtain promising results for relatively small scenes, e. g., indoor environments and scenes with a few objects, but tend to fail for unbounded outdoor scenes with a single image as input.
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.
GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting
We present GALA3D, generative 3D GAussians with LAyout-guided control, for effective compositional text-to-3D generation.
