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Found 29 papers, 11 papers with code
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition
Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network
Dynamic texture synthesis aims to generate sequences that are visually similar to a reference video texture and exhibit specific stationary properties in time.
Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)
Thispaperaimstoresearchandimplementa real-timevideotargettrackingalgorithmbasedon ConvolutionalNeuralNetworks(CNN), enhancingthe accuracyandrobustnessoftargettrackingincomplex scenarios. Addressingthelimitationsoftraditionaltracking algorithmsinhandlingissuessuchastargetocclusion, morphologicalchanges, andbackgroundinterference, our approachintegratestargetdetectionandtrackingstrategies. It continuouslyupdatesthetargetmodelthroughanonline learningmechanismtoadapttochangesinthetarget's appearance. Experimentalresultsdemonstratethat, when dealingwithsituationsinvolvingrapidmotion, partial occlusion, andcomplexbackgrounds, theproposedalgorithm exhibitshighertrackingsuccessratesandlowerfailurerates comparedtoseveralmainstreamtrackingalgorithms. This studysuccessfullyappliesCNNtoreal-timevideotarget tracking, improvingtheaccuracyandstabilityofthetracking algorithmwhilemaintaininghighprocessingspeeds, thus meetingthedemandsofreal-timeapplications. Thisalgorithm isexpectedtoprovidenewsolutionsfortargettrackingtasksin videosurveillanceandintelligenttransportationdomains.
How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph development.
In-Context Editing: Learning Knowledge from Self-Induced Distributions
In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded generalization, and unnatural language generation.
RAM: Towards an Ever-Improving Memory System by Learning from Communications
We introduce an innovative RAG-based framework with an ever-improving memory.
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).
An Efficient Training Approach for Very Large Scale Face Recognition
This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Large Scale Product Graph Construction for Recommendation in E-commerce
Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity.
Weakly Supervised Construction of ASR Systems with Massive Video Data
Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing
The dataset is publicly accessible to the community for boosting the advance of face parsing. 1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.
Ranked #9 on
Face Parsing
on LaPa
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.
Co-Mining: Deep Face Recognition With Noisy Labels
Face recognition has achieved significant progress with the growing scale of collected datasets, which empowers us to train strong convolutional neural networks (CNNs).
A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark
Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks.
Grand Challenge of 106-Point Facial Landmark Localization
However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.
Prediction-Tracking-Segmentation
We introduce a prediction driven method for visual tracking and segmentation in videos.
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.
Multi-Source Pointer Network for Product Title Summarization
For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.
Ensemble Soft-Margin Softmax Loss for Image Classification
On the other hand, the learned classifier of softmax loss is weak.
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.
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
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
Exclusivity-Consistency Regularized Multi-View Subspace Clustering
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups.
Deep Person Re-Identification with Improved Embedding and Efficient Training
However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows.
Adaptively Unified Semi-Supervised Dictionary Learning With Active Points
In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously.
