Search Results for author:
Found 34 papers, 14 papers with code
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality.
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models.
PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework
Generative model based image lossless compression algorithms have seen a great success in improving compression ratio.
Split Hierarchical Variational Compression
Secondly, we define our coding framework, the autoregressive initial bits, that flexibly supports parallel coding and avoids -- for the first time -- many of the practicalities commonly associated with bits-back coding.
Memory Replay with Data Compression for Continual Learning
In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch.
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
In this paper, we discuss lossless compression using normalizing flows which have demonstrated a great capacity for achieving high compression ratios.
Ranked #1 on
Image Compression
on ImageNet32
Nonlinear ICA Using Volume-Preserving Transformations
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression
We also propose a lossless compression algorithm based on iVPF.
Understanding and Exploring the Network with Stochastic Architectures
In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.
Loss Function Search for Face Recognition
In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.
Ranked #38 on
Object Detection
on COCO-O
Mis-classified Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild
To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild.
Ranked #3 on
Object Detection
on WiderPerson
(mMR metric)
Relational Learning for Joint Head and Human Detection
Head and human detection have been rapidly improved with the development of deep convolutional neural networks.
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes
Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.
DARTS+: Improved Differentiable Architecture Search with Early Stopping
Therefore, we propose a simple and effective algorithm, named "DARTS+", to avoid the collapse and improve the original DARTS, by "early stopping" the search procedure when meeting a certain criterion.
RefineFace: Refinement Neural Network for High Performance Face Detection
To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search space for subsequent classifier, then SML is applied to better distinguish faces from background at various scales and FSM is introduced to let the backbone learn more discriminative features for classification.
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Pairwise Teacher-Student Network for Semi-Supervised Hashing
Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed.
Improved Selective Refinement Network for Face Detection
With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.
Support Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Ranked #1 on
Face Identification
on Trillion Pairs Dataset
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.
ScratchDet: Training Single-Shot Object Detectors from Scratch
Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.
Selective Refinement Network for High Performance Face Detection
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.
Ranked #1 on
Face Detection
on PASCAL Face
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other.
Ranked #10 on
Pedestrian Detection
on Caltech
(using extra training data)
Semantic Cluster Unary Loss for Efficient Deep Hashing
The resultant hashcodes form several compact clusters, which means hashcodes in the same cluster have similar semantic information.
Ensemble Soft-Margin Softmax Loss for Image Classification
On the other hand, the learned classifier of softmax loss is weak.
Single-Shot Refinement Neural Network for Object Detection
For object detection, the two-stage approach (e. g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e. g., SSD) has the advantage of high efficiency.
Ranked #178 on
Object Detection
on COCO test-dev
S3FD: Single Shot Scale-Invariant Face Detector
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.
Ranked #3 on
Face Detection
on PASCAL Face
S$^3$FD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
Ranked #2 on
Face Detection
on PASCAL Face
Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization
Hashing method maps similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed.
