Search Results for author:
Found 22 papers, 7 papers with code
Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better
SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims.
A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation
Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions.
A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications
We address the problem of unsupervised semantic segmentation of outdoor LiDAR point clouds in diverse traffic scenarios.
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models.
Expressing linear equality constraints in feedforward neural networks
Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints.
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds.
Towards Improving the Generation Quality of Autoregressive Slot VAEs
We propose two improvements that strengthen object correlation learning.
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions
Scene flow estimation is therefore converted into the problem of recovering motion from the alignment of probability density functions, which we achieve using a closed-form expression of the classic Cauchy-Schwarz divergence.
Self-Supervised Learning
Self-supervised Scene Flow Estimation
Protum: A New Method For Prompt Tuning Based on "[MASK]"
In response to the above issue, we propose a new \textbf{Pro}mpt \textbf{Tu}ning based on "[\textbf{M}ASK]" (\textbf{Protum}) method in this paper, which constructs a classification task through the information carried by the hidden layer of "[MASK]" tokens and then predicts the labels directly rather than the answer tokens.
Learning Scene Dynamics from Point Cloud Sequences
Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames.
Generating Scenes with Latent Object Models
We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes.
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize.
SparsePipe: Parallel Deep Learning for 3D Point Clouds
Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead.
Quantum frequency doubling in the topological insulator Bi2Se3
We introduce the Berry curvature triple, a high-order moment of the Berry curvature, to explain skew scattering under the threefold rotational symmetry.
Materials Science
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
In this paper, we propose a two-stream Convolutional Network architecture that performs real-time detection, tracking, and near accident detection of road users in traffic video data.
Adaptive Adversarial Attack on Scene Text Recognition
We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e. g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks.
Boosting up Scene Text Detectors with Guided CNN
Deep CNNs have achieved great success in text detection.
Adversarial Examples: Attacks and Defenses for Deep Learning
In this paper, we review recent findings on adversarial examples for deep neural networks, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods.
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead.
Single Shot Text Detector with Regional Attention
Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28].
Ranked #4 on
Scene Text Detection
on COCO-Text
Detecting Text in Natural Image with Connectionist Text Proposal Network
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image.
Reading Scene Text in Deep Convolutional Sequences
We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem.
