Search Results for author:
Found 95 papers, 47 papers with code
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard.
Ranked #1 on
Common Sense Reasoning
on ReCoRD
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space.
Interpreting the Latent Space of GANs for Semantic Face Editing
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
Path-Restore: Learning Network Path Selection for Image Restoration
To leverage this, we propose Path-Restore, a multi-path CNN with a pathfinder that can dynamically select an appropriate route for each image region.
Switchable Whitening for Deep Representation Learning
Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods.
Ranked #6 on
Robust Object Detection
on DWD
A Lightweight Optical Flow CNN -- Revisiting Data Fidelity and Regularization
Over four decades, the majority addresses the problem of optical flow estimation using variational methods.
Ranked #6 on
Optical Flow Estimation
on KITTI 2012
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
An Embarrassingly Simple Approach for Knowledge Distillation
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model.
FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis
In the second stage, they compete in the image domain to render photo-realistic images that contain high diversity but preserve identity.
Deep Network Interpolation for Continuous Imagery Effect Transition
Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect.
Improving On-policy Learning with Statistical Reward Accumulation
Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).
Ranked #2 on
Face Hallucination
on FFHQ 512 x 512 - 16x upscaling
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
Ranked #3 on
All-day Semantic Segmentation
on All-day CityScapes
FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis
Existing methods typically formulate GAN as a two-player game, where a discriminator distinguishes face images from the real and synthesized domains, while a generator reduces its discriminativeness by synthesizing a face of photo-realistic quality.
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
Data for face analysis often exhibit highly-skewed class distribution, i. e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances.
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation.
Ranked #10 on
Optical Flow Estimation
on KITTI 2012
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
Ranked #1 on
Face Identification
on IJB-A
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
Ranked #50 on
Lane Detection
on CULane
Mix-and-Match Tuning for Self-Supervised Semantic Segmentation
The key of this new form of learning is to design a proxy task (e. g. image colorization), from which a discriminative loss can be formulated on unlabeled data.
Learning to Disambiguate by Asking Discriminative Questions
In particular, each tuple consists of a pair of images and 4. 6 discriminative questions (as positive samples) and 5. 9 non-discriminative questions (as negative samples) on average.
Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
Video Object Segmentation with Re-identification
Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module.
Recurrent Scale Approximation for Object Detection in CNN
To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid.
Ranked #3 on
Face Detection
on Annotated Faces in the Wild
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement.
Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion
Person re-identification (ReID) is an important task in video surveillance and has various applications.
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.
Temporal Segment Networks for Action Recognition in Videos
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Ranked #5 on
Video Classification
on COIN
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 #635 on
Image Classification
on ImageNet
Temporal Action Detection with Structured Segment Networks
Detecting actions in untrimmed videos is an important yet challenging task.
Ranked #6 on
Action Recognition
on THUMOS’14
Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascade
Third, in comparison to MC, LC is an end-to-end trainable framework, allowing joint learning of all sub-models.
Ranked #22 on
Semantic Segmentation
on PASCAL VOC 2012 test
A Pursuit of Temporal Accuracy in General Activity Detection
Detecting activities in untrimmed videos is an important but challenging task.
Ranked #29 on
Temporal Action Localization
on ActivityNet-1.3
Video Frame Synthesis using Deep Voxel Flow
We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow.
Ranked #2 on
Video Prediction
on DAVIS 2017
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.
Local Similarity-Aware Deep Feature Embedding
Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric.
Ranked #27 on
Metric Learning
on CUB-200-2011
Image Aesthetic Assessment: An Experimental Survey
This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality.
From Facial Expression Recognition to Interpersonal Relation Prediction
Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data.
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power.
Fashion Landmark Detection in the Wild
Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.
Deep Convolution Networks for Compression Artifacts Reduction
Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring.
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Ranked #3 on
Multimodal Activity Recognition
on EV-Action
Accelerating the Super-Resolution Convolutional Neural Network
As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality.
Deep Cascaded Bi-Network for Face Hallucination
We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD).
Ranked #5 on
Image Super-Resolution
on VggFace2 - 8x upscaling
Deep Learning Markov Random Field for Semantic Segmentation
Semantic segmentation tasks can be well modeled by Markov Random Field (MRF).
Unsupervised Learning of Discriminative Attributes and Visual Representations
Attributes offer useful mid-level features to interpret visual data.
Learning Deep Representation for Imbalanced Classification
We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both inter-cluster and inter-class margins.
DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations
To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
Unconstrained Face Alignment via Cascaded Compositional Learning
We present a practical approach to address the problem of unconstrained face alignment for a single image.
Ranked #19 on
Face Alignment
on AFLW-19
Actionness Estimation Using Hybrid Fully Convolutional Networks
Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location.
Ranked #11 on
Action Detection
on J-HMDB
Discriminative Sparse Neighbor Approximation for Imbalanced Learning
These methods further deteriorate on small, imbalanced data that has a large degree of class overlap.
Sparsifying Neural Network Connections for Face Recognition
This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition.
Deep Learning Strong Parts for Pedestrian Detection
Third, each part detector in DeepParts is a strong detector that can detect pedestrian by observing only a part of a proposal.
WIDER FACE: A Face Detection Benchmark
Face detection is one of the most studied topics in the computer vision community.
Ranked #34 on
Face Detection
on WIDER Face (Medium)
Towards Arbitrary-View Face Alignment by Recommendation Trees
The unified framework seamlessly handles different viewpoints and landmark protocols, and it is trained by optimising directly on landmark locations, thus yielding superior results on arbitrary-view face alignment.
Adjustable Bounded Rectifiers: Towards Deep Binary Representations
This suggests that the semantic structure of a neural network may be manifested through a guided binarization process.
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.
Learning Social Relation Traits from Face Images
Social relation defines the association, e. g, warm, friendliness, and dominance, between two or more people.
Semantic Image Segmentation via Deep Parsing Network
This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts.
Ranked #89 on
Semantic Segmentation
on Cityscapes test
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Updated on 24/09/2015: This update provides preliminary experiment results for fine-grained classification on the surveillance data of CompCars.
Ranked #5 on
Fine-Grained Image Classification
on CompCars
Reading Scene Text in Deep Convolutional Sequences
We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem.
Face alignment by coarse-to-fine shape searching
We present a novel face alignment framework based on coarse-to-fine shape searching.
Ranked #20 on
Face Alignment
on AFLW-19
Cascaded Hand Pose Regression
We extends the previous 2D cascaded object pose regression work [9] in two aspects so that it works better for 3D articulated objects.
Recognize Complex Events From Static Images by Fusing Deep Channels
A considerable portion of web images capture events that occur in our personal lives or social activities.
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
Visual features are of vital importance for human action understanding in videos.
Ranked #59 on
Action Recognition
on HMDB-51
Compression Artifacts Reduction by a Deep Convolutional Network
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
DeepID3: Face Recognition with Very Deep Neural Networks
Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity.
Learning to Recognize Pedestrian Attribute
Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given.
Image Super-Resolution Using Deep Convolutional Networks
We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Ranked #2 on
Video Super-Resolution
on Xiph HD - 4x upscaling
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
Deeply learned face representations are sparse, selective, and robust
(2) Its neurons in higher layers are highly selective to identities and identity-related attributes.
Ranked #1 on
Face Verification
on Oulu-CASIA
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes.
Zeta Hull Pursuits: Learning Nonconvex Data Hulls
Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning.
Pedestrian Detection aided by Deep Learning Semantic Tasks
Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources.
Ranked #30 on
Pedestrian Detection
on Caltech
Deep Learning Face Attributes in the Wild
LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction.
Ranked #6 on
Facial Attribute Classification
on LFWA
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.
Transferring Landmark Annotations for Cross-Dataset Face Alignment
We show extensive results on combining various popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and unseen data alignment.
Learning Deep Representation for Face Alignment with Auxiliary Attributes
In this study, we show that landmark detection or face alignment task is not a single and independent problem.
Ranked #13 on
Unsupervised Facial Landmark Detection
on MAFL
Homophilic Clustering by Locally Asymmetric Geometry
Clustering is indispensable for data analysis in many scientific disciplines.
Deep Learning Multi-View Representation for Face Recognition
Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes.
3D ShapeNets: A Deep Representation for Volumetric Shapes
Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
Ranked #35 on
3D Point Cloud Classification
on ModelNet40
(Mean Accuracy metric)
Deep Learning Face Representation by Joint Identification-Verification
The learned DeepID2 features can be well generalized to new identities unseen in the training data.
Deep Learning Face Representation from Predicting 10,000 Classes
When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.
Realtime and Robust Hand Tracking from Depth
We present a realtime hand tracking system using a depth sensor.
A Learning-to-Rank Approach for Image Color Enhancement
We present a machine-learned ranking approach for automatically enhancing the color of a photograph.
Switchable Deep Network for Pedestrian Detection
In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection.
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
For the first time, the human-level performance in face verification (97. 53%) on LFW is surpassed.
Recover Canonical-View Faces in the Wild with Deep Neural Networks
Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications.
Deep Learning Face Representation from Predicting 10,000 Classes
When learned as classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.
Ranked #6 on
Face Verification
on Labeled Faces in the Wild
Deep Convolutional Network Cascade for Facial Point Detection
At each level, the outputs of multiple networks are fused for robust and accurate estimation.
Measuring Crowd Collectiveness
Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields.
Motionlets: Mid-level 3D Parts for Human Motion Recognition
We postulate three key properties of motionlet for action recognition: high motion saliency, multiple scale representation, and representative-discriminative ability.
Learning the Change for Automatic Image Cropping
Image cropping is a common operation used to improve the visual quality of photographs.
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
Ranked #1 on
Image Clustering
on Coil-20
(Accuracy metric)
Single Image Haze Removal Using Dark Channel Prior
The dark channel prior is a kind of statistics of outdoor haze-free images.
Ranked #1 on
Single Image Haze Removal
on RESIDE
Cyclizing Clusters via Zeta Function of a Graph
A mathematical tool, Zeta function of a graph, is introduced for the integration of all cycles, leading to a structural descriptor of the cluster in determinantal form.
