Deep Active Learning with Noise Stability

Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing methods resort to special learning fashions (e.g. adversarial) or auxiliary models to address this challenge. This tends to result in complex and inefficient pipelines, which would render the methods impractical. In this work, we propose a novel algorithm that leverages noise stability to estimate data uncertainty in a Single-Training Multi-Inference fashion. The key idea is to measure the output derivation from the original observation when the model parameters are randomly perturbed by noise. We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients. Despite its simplicity, our method outperforms the state-of-the-art active learning baselines in various tasks, including computer vision, natural language processing, and structural data analysis.

Graph Gain: A Concave-Hull Based Volumetric Gain for Robotic Exploration

The existing volumetric gain for robotic exploration is calculated in the 3D occupancy map, while the sampling-based exploration method is extended in the reachable (free) space. The inconsistency between them makes the existing calculation of volumetric gain inappropriate for a complete exploration of the environment. To address this issue, we propose a concave-hull based volumetric gain in a sampling-based exploration framework. The concave hull is constructed based on the viewpoints generated by Rapidly-exploring Random Tree (RRT) and the nodes that fail to expand. All space outside this concave hull is considered unknown. The volumetric gain is calculated based on the viewpoints configuration rather than using the occupancy map. With the new volumetric gain, robots can avoid inefficient or even erroneous exploration behavior caused by the inappropriateness of existing volumetric gain calculation methods. Our exploration method is evaluated against the existing state-of-the-art RRT-based method in a benchmark environment. In the evaluated environment, the average running time of our method is about 38.4% of the existing state-of-the-art method and our method is more robust.

Ada-Detector: Adaptive Frontier Detector for Rapid Exploration

In this paper, we propose an efficient frontier detector method based on adaptive Rapidly-exploring Random Tree (RRT) for autonomous robot exploration. Robots can achieve real-time incremental frontier detection when they are exploring unknown environments. First, our detector adaptively adjusts the sampling space of RRT by sensing the surrounding environment structure. The adaptive sampling space can greatly improve the successful sampling rate of RRT (the ratio of the number of samples successfully added to the RRT tree to the number of sampling attempts) according to the environment structure and control the expansion bias of the RRT. Second, by generating non-uniform distributed samples, our method also solves the over-sampling problem of RRT in the sliding windows, where uniform random sampling causes over-sampling in the overlap area between two adjacent sliding windows. In this way, our detector is more inclined to sample in the latest explored area, which improves the efficiency of frontier detection and achieves incremental detection. We validated our method in three simulated benchmark scenarios. The experimental comparison shows that we reduce the frontier detection runtime by about 40% compared with the SOTA method, DSV Planner.

LiDAR Road-Atlas: An Efficient Map Representation for General 3D Urban Environment

In this work, we propose the LiDAR Road-Atlas, a compactable and efficient 3D map representation, for autonomous robot or vehicle navigation in general urban environment. The LiDAR Road-Atlas can be generated by an online mapping framework based on incrementally merging local 2D occupancy grid maps (2D-OGM). Specifically, the contributions of our LiDAR Road-Atlas representation are threefold. First, we solve the challenging problem of creating local 2D-OGM in non-structured urban scenes based on a real-time delimitation of traversable and curb regions in LiDAR point cloud. Second, we achieve accurate 3D mapping in multiple-layer urban road scenarios by a probabilistic fusion scheme. Third, we achieve very efficient 3D map representation of general environment thanks to the automatic local-OGM induced traversable-region labeling and a sparse probabilistic local point-cloud encoding. Given the LiDAR Road-Atlas, one can achieve accurate vehicle localization, path planning and some other tasks. Our map representation is insensitive to dynamic objects which can be filtered out in the resulting map based on a probabilistic fusion. Empirically, we compare our map representation with a couple of popular map representation methods in robotics and autonomous driving societies, and our map representation is more favorable in terms of efficiency, scalability and compactness. In addition, we also evaluate localization accuracy extensively given the created LiDAR Road-Atlas representations on several public benchmark datasets. With a 16-channel LiDAR sensor, our method achieves an average global localization errors of 0.26m (translation) and 1.07 degrees (rotation) on the Apollo dataset, and 0.89m (translation) and 1.29 degrees (rotation) on the MulRan dataset, respectively, at 10Hz, which validates the promising performance of our map representation for autonomous driving.

HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud

Place recognition or loop closure detection is one of the core components in a full SLAM system. In this paper, aiming at strengthening the relevancy of local neighboring points and the contextual dependency among global points simultaneously, we investigate the exploitation of transformer-based network for feature extraction, and propose a Hierarchical Transformer for Place Recognition (HiTPR). The HiTPR consists of four major parts: point cell generation, short-range transformer (SRT), long-range transformer (LRT) and global descriptor aggregation. Specifically, the point cloud is initially divided into a sequence of small cells by downsampling and nearest neighbors searching. In the SRT, we extract the local feature for each point cell. While in the LRT, we build the global dependency among all of the point cells in the whole point cloud. Experiments on several standard benchmarks demonstrate the superiority of the HiTPR in terms of average recall rate, achieving 93.71% at top 1% and 86.63% at top 1 on the Oxford RobotCar dataset for example.

Learning Disentangled Representation for One-shot Progressive Face Swapping

Although face swapping has attracted much attention in recent years, it remains a challenging problem. The existing methods leverage a large number of data samples to explore the intrinsic properties of face swapping without taking into account the semantic information of face images. Moreover, the representation of the identity information tends to be fixed, leading to suboptimal face swapping. In this paper, we present a simple yet efficient method named FaceSwapper, for one-shot face swapping based on Generative Adversarial Networks. Our method consists of a disentangled representation module and a semantic-guided fusion module. The disentangled representation module is composed of an attribute encoder and an identity encoder, which aims to achieve the disentanglement of the identity and the attribute information. The identity encoder is more flexible and the attribute encoder contains more details of the attributes than its competitors. Benefiting from the disentangled representation, FaceSwapper can swap face images progressively. In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped area and model the pose and expression more accurately. The experimental results show that our method achieves state-of-the-art results on benchmark datasets with fewer training samples. Our code is publicly available at https://github.com/liqi-casia/FaceSwapper.

Inadequately Pre-trained Models are Better Feature Extractors

Pre-training has been a popular learning paradigm in deep learning era, especially in annotation-insufficient scenario. Better ImageNet pre-trained models have been demonstrated, from the perspective of architecture, by previous research to have better transferability to downstream tasks. However, in this paper, we found that during the same pre-training process, models at middle epochs, which is inadequately pre-trained, can outperform fully trained models when used as feature extractors (FE), while the fine-tuning (FT) performance still grows with the source performance. This reveals that there is not a solid positive correlation between top-1 accuracy on ImageNet and the transferring result on target data. Based on the contradictory phenomenon between FE and FT that better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before softmax layer to provide insightful explanations. Our discoveries suggest that, during pre-training, models tend to first learn spectral components corresponding to large singular values and the residual components contribute more when fine-tuning.

Learning Moving-Object Tracking with FMCW LiDAR

In this paper, we propose a learning-based moving-object tracking method utilizing our newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Compared with most existing commercial LiDAR sensors, our FMCW LiDAR can provide additional Doppler velocity information to each 3D point of the point clouds. Benefiting from this, we can generate instance labels as ground truth in a semi-automatic manner. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances, to improve the tracking quality. Extensive experiments are conducted on our recorded driving data, and the results show that our method outperforms the baseline methods by a large margin.

Yaw-Guided Imitation Learning for Autonomous Driving in Urban Environments

Existing imitation learning methods suffer from low efficiency and generalization ability when facing the road option problem in an urban environment. In this paper, we propose a yaw-guided imitation learning method to improve the road option performance in an end-to-end autonomous driving paradigm in terms of the efficiency of exploiting training samples and adaptability to changing environments. Specifically, the yaw information is provided by the trajectory of the navigation map. Our end-to-end architecture, Yaw-guided Imitation Learning with ResNet34 Attention (YILRatt), integrates the ResNet34 backbone and attention mechanism to obtain an accurate perception. It does not need high precision maps and realizes fully end-to-end autonomous driving given the yaw information provided by a consumer-level GPS receiver. By analyzing the attention heat maps, we can reveal some causal relationship between decision-making and scene perception, where, in particular, failure cases are caused by erroneous perception. We collect expert experience in the Carla 0.9.11 simulator and improve the benchmark CoRL2017 and NoCrash. Experimental results show that YILRatt has a 26.27% higher success rate than the SOTA CILRS. The code, dataset, benchmark and experimental results can be found at https://github.com/Yandong024/Yaw-guided-IL.git

Multi-View TRGRU: Transformer based Spatiotemporal Model for Short-Term Metro Origin-Destination Matrix Prediction

Accurate prediction of short-term OD Matrix (i.e. the distribution of passenger flows from various origins to destinations) is a crucial task in metro systems. It is highly challenging due to the constantly changing nature of many impacting factors and the real-time de- layed data collection problem. Recently, some deep learning-based models have been proposed for OD Matrix forecasting in ride- hailing and high way traffic scenarios. However, these models can not sufficiently capture the complex spatiotemporal correlation between stations in metro networks due to their different prior knowledge and contextual settings. In this paper we propose a hy- brid framework Multi-view TRGRU to address OD metro matrix prediction. In particular, it uses three modules to model three flow change patterns: recent trend, daily trend, weekly trend. In each module, a multi-view representation based on embedding for each station is constructed and fed into a transformer based gated re- current structure so as to capture the dynamic spatial dependency in OD flows of different stations by a global self-attention mecha- nism. Extensive experiments on three large-scale, real-world metro datasets demonstrate the superiority of our Multi-view TRGRU over other competitors.