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Found 44 papers, 17 papers with code
CoMamba: Real-time Cooperative Perception Unlocked with State Space Models
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy.
Light the Night: A Multi-Condition Diffusion Framework for Unpaired Low-Light Enhancement in Autonomous Driving
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems.
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions
In this paper, we propose a Domain Generalization based approach, named V2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions.
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.
Ranked #1 on
Oriented Object Detection
on EVD4UAV
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories.
Vehicle Behavior Prediction by Episodic-Memory Implanted NDT
In this work, we explore the interpretability of behavior prediction of target vehicles by an Episodic Memory implanted Neural Decision Tree (abbrev.
Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources
However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system.
AdvGPS: Adversarial GPS for Multi-Agent Perception Attack
The multi-agent perception system collects visual data from sensors located on various agents and leverages their relative poses determined by GPS signals to effectively fuse information, mitigating the limitations of single-agent sensing, such as occlusion.
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification
Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world.
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
Each individual slice of the volume signifies the relative distances between points and objects within a latent space.
Ranked #5 on
Monocular Depth Estimation
on KITTI Eigen split
SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting
Given the coordinates of a pixel we want to reconstruct, we first collect its neighboring pixels in the input image and extract their detail-enhanced semantic embeddings, unmask-attentional semantic embeddings, importance values, and spatial distances to the desired pixel.
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
To bridge the domain gap and improve the performance of object detection in foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection.
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality
We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap.
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model.
Deep Transfer Learning for Intelligent Vehicle Perception: a Survey
To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception.
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
The emergence of different sensors (Near-Infrared, Depth, etc.)
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception.
Semi-supervised Large-scale Fiber Detection in Material Images with Synthetic Data
Accurate detection of large-scale, elliptical-shape fibers, including their parameters of center, orientation and major/minor axes, on the 2D cross-sectioned image slices is very important for characterizing the underlying cylinder 3D structures in microscopic material images.
Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication
Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared to the ego vehicle so as to improve the perception of the ego vehicle.
Coarse-to-fine Task-driven Inpainting for Geoscience Images
To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks.
Line Drawing Guided Progressive Inpainting of Mural Damage
The proposed approach is evaluated against the current state-of-the-art image inpainting methods.
Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review
This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments.
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent transportation systems, but it is challenging because of the difficulty of modeling the complex interaction relations among the heterogeneous road agents as well as their agent-environment constraints.
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather.
Bridging the Domain Gap for Multi-Agent Perception
Existing multi-agent perception algorithms usually select to share deep neural features extracted from raw sensing data between agents, achieving a trade-off between accuracy and communication bandwidth limit.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
ROMNet: Renovate the Old Memories
Renovating the memories in old photos is an intriguing research topic in computer vision fields.
Benchmarking Shadow Removal for Facial Landmark Detection and Beyond
The observation of this work motivates us to design a novel detection-aware shadow removal framework, which empowers shadow removal to achieve higher restoration quality and enhance the shadow robustness of deployed facial landmark detectors.
Adversarial Relighting Against Face Recognition
To this end, we first propose the physical modelbased adversarial relighting attack (ARA) denoted as albedoquotient-based adversarial relighting attack (AQ-ARA).
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
As one of the state-of-the-art perception approaches, detecting the interested objects in each frame of video surveillance is widely desired by ITS.
AdvHaze: Adversarial Haze Attack
We also visualize the correlation matrices, which inspire us to jointly apply different perturbations to improve the success rate of the attack.
Auto-Exposure Fusion for Single-Image Shadow Removal
We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.
Ranked #6 on
Shadow Removal
on ISTD+
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection
In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.
DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification
Vehicle re-identification (Re-ID) has become a popular research topic owing to its practicability in intelligent transportation systems.
DADA: Driver Attention Prediction in Driving Accident Scenarios
1) With the semantic images, we introduce their semantic context features and verified the manifest promotion effect for helping the driver attention prediction, where the semantic context features are modeled by a graph convolution network (GCN) on semantic images; 2) We fuse the semantic context features of semantic images and the features of RGB frames in an attentive strategy, and the fused details are transferred over frames by a convolutional LSTM module to obtain the attention map of each video frame with the consideration of historical scene variation in driving situations; 3) The superiority of the proposed method is evaluated on our previously collected dataset (named as DADA-2000) and two other challenging datasets with state-of-the-art methods.
An End-to-End Network for Co-Saliency Detection in One Single Image
Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed.
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images
Constructing 3D structures from serial section data is a long standing problem in microscopy.
Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras
In this paper, we attempt to utilize 3D human-skeleton sequences to learn a model that can extract view-invariant motion features from optical flows in different views.
Identifying Designs from Incomplete, Fragmented Cultural Heritage Objects by Curve-Pattern Matching
The challenge to reconstruct and study complete designs is stymied because 1) most fragmentary cultural-heritage objects contain only a small portion of the underlying full design, 2) in the case of a stamping application, the same design may be applied multiple times with spatial overlap on one object, and 3) curve patterns detected on an object are usually incomplete and noisy.
Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences
In particular, the within-group association is modeled by a nonrigid 2D Thin-Plate transform and a sequence of group shrinking, group growing and group merging operations are then developed to refine the composition of each group.
Co-interest Person Detection from Multiple Wearable Camera Videos
Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views.
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.
Feature Sampling Strategies for Action Recognition
Although dense local spatial-temporal features with bag-of-features representation achieve state-of-the-art performance for action recognition, the huge feature number and feature size prevent current methods from scaling up to real size problems.
