Search Results for author:
Found 65 papers, 32 papers with code
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions.
Ranked #27 on
Face Detection
on WIDER Face (Easy)
CosFace: Large Margin Cosine Loss for Deep Face Recognition
The central task of face recognition, including face verification and identification, involves face feature discrimination.
Ranked #3 on
Face Verification
on YouTube Faces DB
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Ranked #1 on
Zero-shot Audio Classification
on VGG-Sound
(using extra training data)
Backdoor Learning: A Survey
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be maliciously changed if the hidden backdoor is activated by attacker-specified triggers.
A Discriminative Feature Learning Approach for Deep Face Recognition
In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model.
Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts
Despite recent advances in image-to-video generation, better controllability and local animation are less explored.
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech.
Boundary-Aware Cascade Networks for Temporal Action Segmentation
To address these problems, we present a new boundary-aware cascade network by introducing two novel components.
Ranked #14 on
Action Segmentation
on GTEA
Target Adaptive Context Aggregation for Video Scene Graph Generation
Specifically, we design an efficient method for frame-level VidSGG, termed as {\em Target Adaptive Context Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context information for relation recognition.
SynFace: Face Recognition with Synthetic Data
We then analyze the underlying causes behind the performance gap, e. g., the poor intra-class variations and the domain gap between synthetic and real face images.
Improving Vision Transformers by Revisiting High-frequency Components
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on
Domain Generalization
on Stylized-ImageNet
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
To reduce such a discrepancy, in this paper we propose a novel algorithm to remove age-related components from features mixed with both identity and age information.
Ranked #3 on
Age-Invariant Face Recognition
on CACDVS
End2End Occluded Face Recognition by Masking Corrupted Features
However, the state-of-the-art general face recognition models do not generalize well to occluded face images, which are exactly the common cases in real-world scenarios.
Squeeze-and-Attention Networks for Semantic Segmentation
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features.
Ranked #6 on
Semantic Segmentation
on PASCAL VOC 2012 test
Sparse Adversarial Attack via Perturbation Factorization
Based on this factorization, we formulate the sparse attack problem as a mixed integer programming (MIP) to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown.
Toward Adversarial Robustness via Semi-supervised Robust Training
In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively.
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks
To this end, we propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN.
Triangle Attack: A Query-efficient Decision-based Adversarial Attack
Decision-based attack poses a severe threat to real-world applications since it regards the target model as a black box and only accesses the hard prediction label.
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
Once attackers maliciously induce high energy consumption and latency time (energy-latency cost) during inference of VLMs, it will exhaust computational resources.
Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network
Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of the face recognition research in the past years.
LARNet: Lie Algebra Residual Network for Face Recognition
We prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation.
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips
The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications.
Detecting Faces Using Region-based Fully Convolutional Networks
Face detection has achieved great success using the region-based methods.
Ranked #2 on
Face Detection
on FDDB
Range Loss for Deep Face Recognition with Long-tail
Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities.
Structure-Regularized Attention for Deformable Object Representation
Capturing contextual dependencies has proven useful to improve the representational power of deep neural networks.
Versatile Weight Attack via Flipping Limited Bits
Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering
Within the experimental models, the T5-based GenCQA with KEPR obtains the best performance, which is up to 60. 91% at the primary canonical metric Inc@3.
Backdoor Attack with Sparse and Invisible Trigger
More importantly, it is not feasible to simply combine existing methods to design an effective sparse and invisible backdoor attack.
Face R-CNN
Faster R-CNN is one of the most representative and successful methods for object detection, and has been becoming increasingly popular in various objection detection applications.
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Practical face recognition has been studied in the past decades, but still remains an open challenge.
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition
Extensive experiments conducted on the three public domain face aging datasets (MORPH Album 2, CACD-VS and FG-NET) have shown the effectiveness of the proposed approach and the value of the constructed CAF dataset on AIFR.
Ranked #3 on
Age-Invariant Face Recognition
on MORPH Album2
A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition
In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition.
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition
In order to address this problem, we propose a novel deep face recognition framework to learn the age-invariant deep face features through a carefully designed CNN model.
Ranked #7 on
Age-Invariant Face Recognition
on CACDVS
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
Detecting Faces Using Inside Cascaded Contextual CNN
Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild.
Range Loss for Deep Face Recognition With Long-Tailed Training Data
Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process.
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
In this paper, we evaluate the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
Facial Attribute Capsules for Noise Face Super Resolution
In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs.
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames.
Controllable Descendant Face Synthesis
Most of the existing methods train models for one-versus-one kin relation, which only consider one parent face and one child face by directly using an auto-encoder without any explicit control over the resemblance of the synthesized face to the parent face.
Rethinking the Trigger of Backdoor Attack
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger, while it performs well on benign samples.
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i. e., shape factor, mean, and variance).
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios.
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Pixel-wise Dense Detector for Image Inpainting
Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts.
Backdoor Attack in the Physical World
We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
Joint Face Image Restoration and Frontalization for Recognition
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
Attacking Adversarial Attacks as A Defense
In this work, we find that the adversarial attacks can also be vulnerable to small perturbations.
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Regional Adversarial Training for Better Robust Generalization
Adversarial training (AT) has been demonstrated as one of the most promising defense methods against various adversarial attacks.
Winnowing Knowledge for Multi-choice Question Answering
However, the current reasoning models suffer from the noises in the retrieved knowledge.
Feature-level Incongruence Reduction for Multimodal Translation
Besides of linguistic features in captions, MNMT allows visual(image) features to be used.
Neural Routing by Memory
However, they use the same procedure sequence for all inputs, regardless of the intermediate features. This paper proffers a simple yet effective idea of constructing parallel procedures and assigning similar intermediate features to the same specialized procedures in a divide-and-conquer fashion.
DGL-GAN: Discriminator Guided Learning for GAN Compression
Moreover, DGL-GAN is also effective in boosting the performance of original uncompressed GANs.
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations
With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i. e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities.
Ranked #1 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Towards Efficient Adversarial Training on Vision Transformers
Vision Transformer (ViT), as a powerful alternative to Convolutional Neural Network (CNN), has received much attention.
Towards In-distribution Compatibility in Out-of-distribution Detection
Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions.
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature extractor is also known as a teacher model.
UFO: Unified Fact Obtaining for Commonsense Question Answering
UFO turns LLMs into knowledge sources and produces relevant facts (knowledge statements) for the given question.
DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation
In recent years, audio-driven 3D facial animation has gained significant attention, particularly in applications such as virtual reality, gaming, and video conferencing.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
