In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operates directly on images and LiDAR scans without requiring any local feature extraction modules. By projecting the sensor data onto the unit sphere, we learn a multi-modal descriptor of partially overlapping scenes using a spherical convolutional neural network. The employed spherical projection model enables the support of arbitrary LiDAR and camera systems readily without losing information. Loop closure candidates are found using a nearest-neighbor lookup in the embedding space. We tackle the problem of correctly identifying the closest place by correlating the candidates' power spectra, obtaining a confidence value per prospect. Our estimate for the correct place corresponds then to the candidate with the highest confidence. We evaluate our proposal w.r.t. state-of-the-art approaches in place recognition using real-world data acquired using different sensors. Our approach can achieve a recall that is up to 10% and 5% higher than for a LiDAR- and vision-based system, respectively, when the sensor setup differs between model training and deployment. Additionally, our place selection can correctly identify up to 95% matches from the candidate set.
Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical databases in the information era it can be assumed that very many outlier galaxies are included in existing and future astronomical databases. However, manual search for these objects is impractical due to the required labor, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the dataset by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis (MS). Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like when demographic or disease covariates are changed. These images can be used for modeling disease progression or used for downstream image processing tasks where controlling for confounders is necessary.
In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global information and capture long-range relationships, which are important for the depth prediction of objects. Therefore, we propose a novel asymmetric non-local attention block with multi-scale sampling to extract depth-wise features. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset, in both 3D object detection and bird's eye view tasks.
Code summarization (CS) is becoming a promising area in recent natural language understanding, which aims to generate sensible annotations automatically for source code and is known as programmer oriented. Previous works attempt to apply structure-based traversal (SBT) or non-sequential models like Tree-LSTM and GNN to learn structural program semantics. They both meet the following drawbacks: 1) it is shown ineffective to incorporate SBT into Transformer; 2) it is limited to capture global information through GNN; 3) it is underestimated to capture structural semantics only using Transformer. In this paper, we propose a novel model based on structure-induced self-attention, which encodes sequential inputs with highly-effective structure modeling. Extensive experiments show that our newly-proposed model achieves new state-of-the-art results on popular benchmarks. To our best knowledge, it is the first work on code summarization that uses Transformer to model structural information with high efficiency and no extra parameters. We also provide a tutorial on how we pre-process.
Optical flow estimation with occlusion or large displacement is a problematic challenge due to the lost of corresponding pixels between consecutive frames. In this paper, we discover that the lost information is related to a large quantity of motion features (more than 40%) computed from the popular discriminative cost-volume feature would completely vanish due to invalid sampling, leading to the low efficiency of optical flow learning. We call this phenomenon the Vanishing Cost Volume Problem. Inspired by the fact that local motion tends to be highly consistent within a short temporal window, we propose a novel iterative Motion Feature Recovery (MFR) method to address the vanishing cost volume via modeling motion consistency across multiple frames. In each MFR iteration, invalid entries from original motion features are first determined based on the current flow. Then, an efficient network is designed to adaptively learn the motion correlation to recover invalid features for lost-information restoration. The final optical flow is then decoded from the recovered motion features. Experimental results on Sintel and KITTI show that our method achieves state-of-the-art performances. In fact, MFR currently ranks second on Sintel public website.
Visual attention is one of the most significant characteristics for selecting and understanding the outside world. The nature complex scenes, including larger redundancy and human vision, can't be processing all information simultaneously because of the information bottleneck. The visual system mainly focuses on dominant parts of the scenes to reduce the input visual redundancy information. It's commonly known as visual attention prediction or visual saliency map. This paper proposes a new saliency prediction architecture inspired by human low-level visual cortex function. The model considered the opponent color channel, wavelet energy map, and contrast sensitivity function for extract image features and maximum approach to real visual neural network function in the brain. The proposed model is evaluated several datasets, including MIT1003, MIT300, TORONTO, and SID4VAM to explain its efficiency. The proposed model results are quantitatively and qualitatively compared to other state-of-the-art salience prediction models and their achieved out-performing of visual saliency prediction.
Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines. This paper unifies these two techniques into generalized distillation, a framework to learn from multiple machines and data representations. We provide theoretical and causal insight about the inner workings of generalized distillation, extend it to unsupervised, semisupervised and multitask learning scenarios, and illustrate its efficacy on a variety of numerical simulations on both synthetic and real-world data.
Sky imaging systems use lenses to acquire images concentrating light beams in an imager. The light beams received by the sky imager have an elevation angle with respect to the normal of the device. This produces that the image pixels contain information from different areas of the sky within the imaging system Field Of View (FOV). The area of the field of view contained in the pixels increases as the elevation angle of the incident light beams decreases. When the sky imagers are mounted on a solar tracker incidence angle of the light beam on a pixel varies over time. This investigation introduces a transformation that projects the original euclidean frame of the imager plane to the geospatial frame atmosphere cross-section plane form when the sky imager field of view intersects the tropopause.
In this paper, we investigate the secrecy performance of underlay cognitive small-cell radio network with unreliable backhaul connections. The secondary cognitive small-cell transmitters are connected to macro base station by wireless backhaul links. The small-cell network is sharing the same spectrum with the primary network ensuring that a desired outage probability constraint in the primary network is always satisfied. We propose an optimal transmitter selection (OTS) scheme for small-cell network to transfer information to the destination. The closed-form expression of secrecy outage probability are derived. Our result shows that increasing the primary transmitter's transmit power and the number of small-cell transmitter can improve the system performance. The backhaul reliability of secondary and the desired outage probability of the primary also have significant impact on the system.