Search Results for author:
Found 10 papers, 6 papers with code
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
This paper thus offers a significant contribution to the field of instruction data generation and fine-tuning models, providing new insights and tools for enhancing performance in code-related tasks.
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model.
Ranked #1 on
Data Augmentation
on ImageNet
FreeEnricher: Enriching Face Landmarks without Additional Cost
To evaluate our method, we manually label the dense landmarks on 300W testset.
Ranked #1 on
Face Alignment
on 300W
General Facial Representation Learning in a Visual-Linguistic Manner
In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner.
Ranked #1 on
Face Parsing
on CelebAMask-HQ
(using extra training data)
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
On the other hand, AAM is an attention module which can get anisotropic attention mask focusing on the region of point and its local edge connected by adjacent points, it has a stronger response in tangent than in normal, which means relaxed constraints in the tangent.
Ranked #5 on
Face Alignment
on 300W
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability.
A $p/2$ Adversary Power Resistant Blockchain Sharding Approach
First, in a non-sharding blockchain, nodes can have different weight (power or stake) to create a consensus, and as such an adversary needs to control half of the overall weight in order to manipulate the system ($p/2$ security level).
Distributed, Parallel, and Cluster Computing Cryptography and Security
An n/2 Byzantine node tolerate Blockchain Sharding approach
Traditional Blockchain Sharding approaches can only tolerate up to n/3 of nodes being adversary because they rely on the hypergeometric distribution to make a failure (an adversary does not have n/3 of nodes globally but can manipulate the consensus of a Shard) hard to happen.
Cryptography and Security Distributed, Parallel, and Cluster Computing 68M12
Noise-Tolerant Paradigm for Training Face Recognition CNNs
Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR).
SeqFace: Make full use of sequence information for face recognition
Deep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years.
Ranked #1 on
Face Verification
on YouTube Faces DB
