Search Results for author:
Found 39 papers, 23 papers with code
Q-SLAM: Quadric Representations for Monocular SLAM
This focus results in a significant disconnect between NeRF applications, i. e., novel-view synthesis and the requirements of SLAM.
PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
To remedy this, recent work has proposed learning model and score function parameters using data to directly optimize the efficiency of the ICP prediction sets.
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators
Instruction-based language modeling has received significant attention in pretrained language models.
Improving Adaptive Online Learning Using Refined Discretization
Motivated by the pursuit of instance optimality, we propose a new algorithm that simultaneously achieves ($i$) the AdaGrad-style second order gradient adaptivity; and ($ii$) the comparator norm adaptivity also known as "parameter freeness" in the literature.
Toward Intelligent and Efficient 6G Networks: JCSC Enabled On-Purpose Machine Communications
Driven by the vision of "intelligent connection of everything" toward 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches.
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples
Recent studies have revealed the vulnerability of pre-trained language models to adversarial attacks.
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation
Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability.
Few-shot Font Generation by Learning Style Difference and Similarity
To address this issue, we propose a novel font generation approach by learning the Difference between different styles and the Similarity of the same style (DS-Font).
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization Feature Space
We show that the cycle-consistency regularization helps to minimize the volume of the transition matrix T indirectly without exploiting the estimated noisy class posterior, which could further encourage the estimated transition matrix T to converge to its optimal solution.
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework
Our experimental results on three classification tasks and nine public datasets show that BootAug addresses the performance drop problem and outperforms state-of-the-art text augmentation methods.
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+5
Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from 2D and 3D Semantic Keypoints
We consider an active shape model, where -- for an object category -- we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape are estimated from 2D or 3D keypoints via non-convex optimization.
Evolutionary Multi-Task Injection Testing on Web Application Firewalls
In this paper, we propose DaNuoYi, an automatic injection testing tool that simultaneously generates test inputs for multiple types of injection attacks on a WAF.
Combining dynamic local context focus and dependency cluster attention for aspect-level sentiment classification
The DLCF can dynamically capture the range of local context based on the different max distance from the target aspect term to its context words and the DCA allows the model to pay more attention to the cluster which is more critical for sentiment classification.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation
Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification.
Adversarial Defense
Aspect-Based Sentiment Analysis (ABSA)
+2
Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization
Our third contribution is to solve the SDP relaxations at an unprecedented scale and accuracy by presenting STRIDE, a solver that blends global descent on the convex SDP with fast local search on the nonconvex POP.
An Inexact Projected Gradient Method with Rounding and Lifting by Nonlinear Programming for Solving Rank-One Semidefinite Relaxation of Polynomial Optimization
In particular, we first design a globally convergent inexact projected gradient method (iPGM) for solving the SDP that serves as the backbone of our framework.
Optimal Pose and Shape Estimation for Category-level 3D Object Perception
Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation.
Dynamical Pose Estimation
We study the problem of aligning two sets of 3D geometric primitives given known correspondences.
Self-supervised Geometric Perception
We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e. g., camera poses, rigid transformations).
ROBIN: a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants
We also show that in practice the maximum k-core of the compatibility graph provides an approximation of the maximum clique, while being faster to compute in large problems.
Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets.
Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications
We extend ADAPT and GNC to the case where the user does not have prior knowledge of the inlier-noise statistics (or the statistics may vary over time) and is unable to guess a reasonable threshold to separate inliers from outliers (as the one commonly used in RANSAC).
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
We propose the first general and practical framework to design certifiable algorithms for robust geometric perception in the presence of a large amount of outliers.
A Dynamical Perspective on Point Cloud Registration
We provide a dynamical perspective on the classical problem of 3D point cloud registration with correspondences.
TEASER: Fast and Certifiable Point Cloud Registration
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences.
A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction
Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC).
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval-2014 Task-4
(using extra training data)
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks
We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image.
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
In this paper, we enable the simultaneous use of non-minimal solvers and robust estimation by providing a general-purpose approach for robust global estimation, which can be applied to any problem where a non-minimal solver is available for the outlier-free case.
A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with Outliers
Our first contribution is to formulate the Wahba problem using a Truncated Least Squares (TLS) cost that is insensitive to a large fraction of spurious correspondences.
A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates
We propose a robust approach for the registration of two sets of 3D points in the presence of a large amount of outliers.
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners
In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP).
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection.
An Empirical Study of Recent Face Alignment Methods
In this paper, we carry out a rigorous evaluation of these methods by making the following contributions: 1) we proposes a new evaluation metric for face alignment on a set of images, i. e., area under error distribution curve within a threshold, AUC$_\alpha$, given the fact that the traditional evaluation measure (mean error) is very sensitive to big alignment error.
Human and Sheep Facial Landmarks Localisation by Triplet Interpolated Features
Furthermore, we study the impact of training data imbalance on model performance and propose a training sample augmentation scheme that produces more initialisations for training samples from the minority.
Face Alignment Assisted by Head Pose Estimation
In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation.
Mirror, mirror on the wall, tell me, is the error small?
Our experiments lead to several interesting findings: 1) Surprisingly, most of state of the art methods struggle to preserve the mirror symmetry, despite the fact that they do have very similar overall performance on the original and mirror images; 2) the low mirrorability is not caused by training or testing sample bias - all algorithms are trained on both the original images and their mirrored versions; 3) the mirror error is strongly correlated to the localization/alignment error (with correlation coefficients around 0. 7).
Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction
Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications.
