Search Results for author:
Found 52 papers, 16 papers with code
Dataset Pruning: Reducing Training Data by Examining Generalization Influence
To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct a smallest subset of training data that yields strictly constrained generalization gap.
Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos
Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction.
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training
We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well.
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.
Sample Efficiency of Data Augmentation Consistency Regularization
Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data.
Semantically Contrastive Learning for Low-light Image Enhancement
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
PartImageNet: A Large, High-Quality Dataset of Parts
To help address this problem, we propose PartImageNet, a large, high-quality dataset with part segmentation annotations.
Does Preprocessing Help Training Over-parameterized Neural Networks?
The classical training method requires paying $\Omega(mnd)$ cost for both forward computation and backward computation, where $m$ is the width of the neural network, and we are given $n$ training points in $d$-dimensional space.
Speeding up Deep Model Training by Sharing Weights and Then Unsharing
Our approach exploits the special structure of BERT that contains a stack of repeated modules (i. e., transformer encoders).
Objects in Semantic Topology
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently.
Theoretical Analysis of Consistency Regularization with Limited Augmented Data
Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data.
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning
We design a new instance-to-track matching objective to learn appearance embedding that compares a candidate detection to the embedding of the tracks persisted in the tracker.
Estimating Instance-dependent Label-noise Transition Matrix using DNNs
Traditionally, the transition from clean distribution to noisy distribution (i. e., clean label transition matrix) has been widely exploited to learn a clean label classifier by employing the noisy data.
Compatibility-aware Heterogeneous Visual Search
Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery images.
An Efficient Multitask Neural Network for Face Alignment, Head Pose Estimation and Face Tracking
Moreover, we generate a cheap heatmap based on the face alignment result and fuse it with features to improve the performance of the other two tasks.
Linear Bandit Algorithms with Sublinear Time Complexity
We propose to accelerate existing linear bandit algorithms to achieve per-step time complexity sublinear in the number of arms $K$.
Combinatorial Bandits without Total Order for Arms
Specifically, we focus on a challenging setting where 1) the reward distribution of an arm depends on the set $s$ it is part of, and crucially 2) there is \textit{no total order} for the arms in $\mathcal{A}$.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
MirrorAlign: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning
Recently, the performance of the neural word alignment models has exceeded that of statistical models.
Free Lunch for Few-shot Learning: Distribution Calibration
In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier.
Speeding up Deep Learning Training by Sharing Weights and Then Unsharing
It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks.
Unsupervised Word Alignment via Cross-Lingual Contrastive Learning
Recently, the performance of the neural word alignment models has exceeded that of statistical models.
Real-space construction of crystalline topological superconductors and insulators in 2D interacting fermionic systems
The construction and classification of crystalline symmetry protected topological (SPT) phases in interacting bosonic and fermionic systems have been intensively studied in the past few years.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics Mathematical Physics Mathematical Physics
The ANTARES Astronomical Time-Domain Event Broker
We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts.
Instrumentation and Methods for Astrophysics
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
SMOT: Single-Shot Multi Object Tracking
We combine this scheme with SSD detectors by proposing a novel tracking anchor assignment module.
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.
Adaptive Semantic-Visual Tree for Hierarchical Embeddings
To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously.
Single-View 3D Object Reconstruction from Shape Priors in Memory
Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image.
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space
In this paper, we provide a new algorithm - Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space.
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
A structure predictor is proposed to predict the missing face structural information temporally, which serves as a geometry prior.
Learning Actions from Human Demonstration Video for Robotic Manipulation
However, the general video captioning methods focus more on the understanding of the full frame, lacking of consideration on the specific object of interests in robotic manipulations.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Region Proposal by Guided Anchoring
State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.
Ranked #1 on
Region Proposal
on COCO test-dev
Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data
It also demonstrates the outstanding performance of these proposed models as well as other state of the art machine learning models when applied to medical research problems and other real-world large-scale systems, reveals the great potential of statistical relational learning for exploring the structured health-related data to facilitate medical research.
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling
We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful.
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
By utilising boundary information of 300-W dataset, our method achieves 3. 92% mean error with 0. 39% failure rate on COFW dataset, and 1. 25% mean error on AFLW-Full dataset.
Ranked #2 on
Face Alignment
on AFLW-19
(using extra training data)
Optimizing Video Object Detection via a Scale-Time Lattice
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
Leveraging intra and inter-dataset variations for robust face alignment
Face alignment is a critical topic in the computer vision community.
Ranked #23 on
Face Alignment
on WFLW
Face Detection through Scale-Friendly Deep Convolutional Networks
Specifically, our method achieves 76. 4 average precision on the challenging WIDER FACE dataset and 96% recall rate on the FDDB dataset with 7 frames per second (fps) for 900 * 1300 input image.
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion.
Ranked #358 on
Image Classification
on ImageNet
Hand3D: Hand Pose Estimation using 3D Neural Network
We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image.
Faceness-Net: Face Detection through Deep Facial Part Responses
We propose a deep convolutional neural network (CNN) for face detection leveraging on facial attributes based supervision.
Joint Hand Detection and Rotation Estimation by Using CNN
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
Application of Statistical Relational Learning to Hybrid Recommendation Systems
In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system.
WIDER FACE: A Face Detection Benchmark
Face detection is one of the most studied topics in the computer vision community.
Ranked #18 on
Face Detection
on WIDER Face (Medium)
From Facial Parts Responses to Face Detection: A Deep Learning Approach
In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.
