arXiv subjects

Jingyi Xu

Publications and source records attributed to Jingyi Xu.

85 records · Page 5Linked to original sources

arXiv2023

Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not contain the objects perfectly but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this crop-related variability requires abundant training data, which is not available in few-shot settings. To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity. The main idea is to transform the latent space such latent codes with different norms represent different crop-related variations. This allows us to generate features with increased crop-related diversity in difficulty levels by simply varying the latent norm. In particular, each latent code is rescaled such that its norm linearly correlates with the IoU score of the input crop w.r.t. the ground-truth box. Here the IoU score is a proxy that represents the difficulty level of the crop. We train this VAE model on base classes conditioned on the semantic code of each class and then use the trained model to generate features for novel classes. In our experiments our generated features consistently improve state-of-the-art few-shot object detection methods on the PASCAL VOC and MS COCO datasets.

cs.CV↗

arXiv2023

Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Gröbner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall $93.2\%$ accuracy, which significantly outperforms the current state-of-the-art accuracy of $77.0\%$ and exceeds human performance at $84.4\%$ accuracy.

cs.CV↗

arXiv2022

SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data

Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this paper, we tackle the problem of place recognition based on sequential 3D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor. The implementation of our method is released as open source at: https://github.com/BIT-MJY/SeqOT.

cs.CV↗

arXiv2022

Generating Representative Samples for Few-Shot Classification

Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model. We train this CVAE model on base classes and use it to generate features for novel classes. More importantly, we guide this VAE to strictly generate representative samples by removing non-representative samples from the base training set when training the CVAE model. We show that this training scheme enhances the representativeness of the generated samples and therefore, improves the few-shot classification results. Experimental results show that our method improves three FSL baseline methods by substantial margins, achieving state-of-the-art few-shot classification performance on miniImageNet and tieredImageNet datasets for both 1-shot and 5-shot settings. Code is available at: https://github.com/cvlab-stonybrook/fsl-rsvae.

cs.CV↗

arXiv2022

A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles

The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.

cs.LG↗

arXiv2022

FedCorr: Multi-Stage Federated Learning for Label Noise Correction

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL. In this paper, we propose $\texttt{FedCorr}$, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. In particular, (1) $\texttt{FedCorr}$ dynamically identifies noisy clients by exploiting the dimensionalities of the model prediction subspaces independently measured on all clients, and then identifies incorrect labels on noisy clients based on per-sample losses. To deal with data heterogeneity and to increase training stability, we propose an adaptive local proximal regularization term that is based on estimated local noise levels. (2) We further finetune the global model on identified clean clients and correct the noisy labels for the remaining noisy clients after finetuning. (3) Finally, we apply the usual training on all clients to make full use of all local data. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that $\texttt{FedCorr}$ is robust to label noise and substantially outperforms the state-of-the-art methods at multiple noise levels.

cs.LG↗

arXiv2021

Variational Feature Disentangling for Fine-Grained Few-Shot Classification

Fine-grained few-shot recognition often suffers from the problem of training data scarcity for novel categories.The network tends to overfit and does not generalize well to unseen classes due to insufficient training data. Many methods have been proposed to synthesize additional data to support the training. In this paper, we focus one enlarging the intra-class variance of the unseen class to improve few-shot classification performance. We assume that the distribution of intra-class variance generalizes across the base class and the novel class. Thus, the intra-class variance of the base set can be transferred to the novel set for feature augmentation. Specifically, we first model the distribution of intra-class variance on the base set via variational inference. Then the learned distribution is transferred to the novel set to generate additional features, which are used together with the original ones to train a classifier. Experimental results show a significant boost over the state-of-the-art methods on the challenging fine-grained few-shot image classification benchmarks.

cs.CV↗

arXiv2021

Training Classifiers that are Universally Robust to All Label Noise Levels

For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise levels, they encounter significant performance reduction at high noise levels, or even at medium noise levels when the label noise is asymmetric. To train classifiers that are universally robust to all noise levels, and that are not sensitive to any variation in the noise model, we propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as "positive", while the remaining noisy subset is treated as "unlabeled". Our framework consists of the following two components: (1) We shall generate, via iterative updates, an augmented clean subset with additional reliable "positive" samples filtered from "unlabeled" samples; (2) We shall train a teacher model on this larger augmented clean set. With the guidance of the teacher model, we then train a student model on the whole dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic label noise at multiple noise levels for both symmetric and asymmetric noise. The results show that our framework generally outperforms at medium to high noise levels. We also evaluated our framework on Clothing1M, a real-world noisy dataset, and we achieved 2.94% improvement in accuracy over existing state-of-the-art methods.

cs.LG↗

arXiv2021

6DLS: Modeling Nonplanar Frictional Surface Contacts for Grasping using 6D Limit Surfaces

Robot grasping with deformable gripper jaws results in nonplanar surface contacts if the jaws deform to the nonplanar local geometry of an object. The frictional force and torque that can be transmitted through a nonplanar surface contact are both three-dimensional, resulting in a six-dimensional frictional wrench (6DFW). Applying traditional planar contact models to such contacts leads to over-conservative results as the models do not consider the nonplanar surface geometry and only compute a three-dimensional subset of the 6DFW. To address this issue, we derive the 6DFW for nonplanar surfaces by combining concepts of differential geometry and Coulomb friction. We also propose two 6D limit surface (6DLS) models, generalized from well-known three-dimensional LS (3DLS) models, which describe the friction-motion constraints for a contact. We evaluate the 6DLS models by fitting them to the 6DFW samples obtained from six parametric surfaces and 2,932 meshed contacts from finite element method simulations of 24 rigid objects. We further present an algorithm to predict multicontact grasp success by building a grasp wrench space with the 6DLS model of each contact. To evaluate the algorithm, we collected 1,035 physical grasps of ten 3D-printed objects with a KUKA robot and a deformable parallel-jaw gripper. In our experiments, the algorithm achieves 66.8% precision, a metric inversely related to false positive predictions, and 76.9% recall, a metric inversely related to false negative predictions. The 6DLS models increase recall by up to 26.1% over 3DLS models with similar precision.

cs.RO↗

arXiv2020

Learning Clusterable Visual Features for Zero-Shot Recognition

In zero-shot learning (ZSL), conditional generators have been widely used to generate additional training features. These features can then be used to train the classifiers for testing data. However, some testing data are considered "hard" as they lie close to the decision boundaries and are prone to misclassification, leading to performance degradation for ZSL. In this paper, we propose to learn clusterable features for ZSL problems. Using a Conditional Variational Autoencoder (CVAE) as the feature generator, we project the original features to a new feature space supervised by an auxiliary classification loss. To further increase clusterability, we fine-tune the features using Gaussian similarity loss. The clusterable visual features are not only more suitable for CVAE reconstruction but are also more separable which improves classification accuracy. Moreover, we introduce Gaussian noise to enlarge the intra-class variance of the generated features, which helps to improve the classifier's robustness. Our experiments on SUN,CUB, and AWA2 datasets show consistent improvement over previous state-of-the-art ZSL results by a large margin. In addition to its effectiveness on zero-shot classification, experiments show that our method to increase feature clusterability benefits few-shot learning algorithms as well.

cs.CV↗

arXiv2020

GOMP: Grasp-Optimized Motion Planning for Bin Picking

Rapid and reliable robot bin picking is a critical challenge in automating warehouses, often measured in picks-per-hour (PPH). We explore increasing PPH using faster motions based on optimizing over a set of candidate grasps. The source of this set of grasps is two-fold: (1) grasp-analysis tools such as Dex-Net generate multiple candidate grasps, and (2) each of these grasps has a degree of freedom about which a robot gripper can rotate. In this paper, we present Grasp-Optimized Motion Planning (GOMP), an algorithm that speeds up the execution of a bin-picking robot's operations by incorporating robot dynamics and a set of candidate grasps produced by a grasp planner into an optimizing motion planner. We compute motions by optimizing with sequential quadratic programming (SQP) and iteratively updating trust regions to account for the non-convex nature of the problem. In our formulation, we constrain the motion to remain within the mechanical limits of the robot while avoiding obstacles. We further convert the problem to a time-minimization by repeatedly shorting a time horizon of a trajectory until the SQP is infeasible. In experiments with a UR5, GOMP achieves a speedup of 9x over a baseline planner.

cs.RO↗

arXiv2019

Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects

Robot grasping of deformable hollow objects such as plastic bottles and cups is challenging as the grasp should resist disturbances while minimally deforming the object so as not to damage it or dislodge liquids. We propose minimal work as a novel grasp quality metric that combines wrench resistance and the object deformation. We introduce an efficient algorithm to compute required work to resist an external wrench for a manipulation task by solving a linear program. The algorithm first computes the minimum required grasp force and an estimation of the gripper jaw displacements based on the object deformability at different locations measured with physical experiments. The work done by the jaws is the product of the grasp force and the displacements. The grasp quality metric is computed based on the required work under perturbations of grasp poses to address uncertainties in actuation. We collect 460 physical grasps with a UR5 robot and a Robotiq gripper. Physical experiments suggest the minimal work quality metric reaches 74.2% balanced accuracy and is up to 24.2% higher than classical wrench-based quality metrics, where the balanced accuracy is the raw accuracy normalized by the number of successful and failed real-world grasps.

cs.RO↗

arXiv2018

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that it effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.

cs.AI↗