Search Results for author:
Found 252 papers, 119 papers with code
Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes.
Ranked #4 on
Video Semantic Segmentation
on Cityscapes val
Gradient Harmonized Single-stage Detector
Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i. e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples.
Ranked #165 on
Object Detection
on COCO test-dev
Deformable DETR: Deformable Transformers for End-to-End Object Detection
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance.
Ranked #6 on
2D Object Detection
on SARDet-100K
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.
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
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.
Ranked #6 on
Face Verification
on Labeled Faces in the Wild
Context Encoding for Semantic Segmentation
In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
Human pose estimation has achieved significant progress in recent years.
Ranked #23 on
Pose Estimation
on COCO test-dev
(using extra training data)
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications.
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields.
Ranked #2 on
3D Object Detection
on KITTI Cars Easy val
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state.
Ranked #1 on
Instance Segmentation
on COCO test-dev
(AP50 metric, using extra
training data)
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
In this paper, we propose Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance.
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.
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
Ranked #5 on
Text-to-Image Generation
on Oxford 102 Flowers
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Ranked #3 on
Text-to-Image Generation
on Oxford 102 Flowers
(Inception score metric)
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
In this paper, we propose PointRCNN for 3D object detection from raw point cloud.
Ranked #2 on
Object Detection
on KITTI Cars Moderate
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.
Ranked #78 on
Self-Supervised Image Classification
on ImageNet
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Ranked #3 on
Person Re-Identification
on CUHK03
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
Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
While speech content information can be defined by learning the intrinsic synchronization between audio-visual modalities, we identify that a pose code will be complementarily learned in a modulated convolution-based reconstruction framework.
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
Joint Detection and Identification Feature Learning for Person Search
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.
Ranked #9 on
Person Re-Identification
on CUHK03
ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild
We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts.
Ranked #2 on
2D Human Pose Estimation
on COCO-WholeBody
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
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions.
3D Object Detection for Autonomous Driving: A Comprehensive Survey
Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving.
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods.
Multi-Context Attention for Human Pose Estimation
We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.
Ranked #8 on
Pose Estimation
on Leeds Sports Poses
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
Revisiting the Sibling Head in Object Detector
The ``shared head for classification and localization'' (sibling head), firstly denominated in Fast RCNN~\cite{girshick2015fast}, has been leading the fashion of the object detection community in the past five years.
Ranked #67 on
Object Detection
on COCO test-dev
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Temporal and contextual information of videos are not fully investigated and utilized.
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.
Group-wise Correlation Stereo Network
Previous works built cost volumes with cross-correlation or concatenation of left and right features across all disparity levels, and then a 2D or 3D convolutional neural network is utilized to regress the disparity maps.
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.
Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks
The model is pre-trained on several uni-modal and multi-modal tasks, and evaluated on a variety of downstream tasks, including novel tasks that did not appear in the pre-training stage.
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
To mitigate such interference, we introduce the Conditional Mixture-of-Experts (Conditional MoEs) to generalist models.
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on
Pedestrian Attribute Recognition
on RAP
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
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations.
Scene Graph Generation from Objects, Phrases and Region Captions
Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations, and other context information.
Ranked #2 on
Object Detection
on Visual Genome
Learning Feature Pyramids for Human Pose Estimation
We investigate our method on two standard benchmarks for human pose estimation.
Ranked #6 on
Pose Estimation
on Leeds Sports Poses
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Generating scene graph to describe all the relations inside an image gains increasing interests these years.
Ranked #1 on
Scene Graph Generation
on VRD
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
Our results show that training deep neural networks with the AdaCos loss is stable and able to achieve high face recognition accuracy.
Ranked #6 on
Face Verification
on MegaFace
Pose for Everything: Towards Category-Agnostic Pose Estimation
In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition.
Ranked #4 on
2D Pose Estimation
on MP-100
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
Object Detection from Video Tubelets with Convolutional Neural Networks
Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation.
Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer
Vision transformers have achieved great successes in many computer vision tasks.
Ranked #4 on
2D Human Pose Estimation
on COCO-WholeBody
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
KBNet: Kernel Basis Network for Image Restoration
In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggregation.
Ranked #1 on
Color Image Denoising
on McMaster sigma50
Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance.
3D Human Mesh Regression with Dense Correspondence
This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i. e. a 2D space used for texture mapping of 3D mesh).
Ranked #1 on
3D Human Reconstruction
on Surreal
End-to-End Object Detection with Adaptive Clustering Transformer
In this paper, a novel variant of transformer named Adaptive Clustering Transformer(ACT) has been proposed to reduce the computation cost for high-resolution input.
Fast Convergence of DETR with Spatially Modulated Co-Attention
The recently proposed Detection Transformer (DETR) model successfully applies Transformer to objects detection and achieves comparable performance with two-stage object detection frameworks, such as Faster-RCNN.
Fast Convergence of DETR with Spatially Modulated Co-Attention
However, DETR suffers from its slow convergence.
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Depth cues have been proved very useful in various computer vision and robotic tasks.
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This paper addresses the problem of depth estimation from a single still image.
Ranked #14 on
Depth Estimation
on NYU-Depth V2
Frozen CLIP Models are Efficient Video Learners
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos.
Ranked #26 on
Action Classification
on Kinetics-400
(using extra training data)
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Few-shot learning is an important area of research.
Person Search with Natural Language Description
Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance.
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.
Ranked #6 on
Multi-Label Classification
on NUS-WIDE
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization
The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses.
Ranked #1 on
6D Pose Estimation using RGB
on LineMOD
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
Semantic image synthesis aims at generating photorealistic images from semantic layouts.
CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Ranked #9 on
Image Retrieval
on Flickr30K 1K test
DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
In this work, we delve into the potential of large language models (LLMs) in autonomous driving (AD).
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.
IDR: Self-Supervised Image Denoising via Iterative Data Refinement
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input.
Feature Intertwiner for Object Detection
We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space.
Ranked #134 on
Object Detection
on COCO test-dev
Rethinking Noise Synthesis and Modeling in Raw Denoising
However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors.
Ranked #2 on
Image Denoising
on SID SonyA7S2 x100
End-to-End Deep Kronecker-Product Matching for Person Re-identification
Person re-identification aims to robustly measure similarities between person images.
Deep Group-shuffling Random Walk for Person Re-identification
Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images.
FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
On the contrary, the soft composition operates by stitching different patches into a whole feature map where pixels in overlapping regions are summed up.
Ranked #3 on
Video Inpainting
on DAVIS
Convolutional neural networks with low-rank regularization
Recently, tensor decompositions have been used for speeding up CNNs.
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector
Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10. 44%, 5. 69%, 5. 97% mAP respectively on the official KITTI benchmark.
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.
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
In this study, we propose a simple yet effective framework for video restoration.
Ranked #1 on
Deblurring
on GoPro
(using extra training data)
Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
It has been proved that combining multiple pre-training strategies and data from various modalities/sources can greatly boost the training of large-scale models.
Ranked #2 on
Semantic Segmentation
on ADE20K
(using extra training data)
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse.
Object Detection in Videos with Tubelet Proposal Networks
Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset.
Demystify Transformers & Convolutions in Modern Image Deep Networks
Our experiments on various tasks and an analysis of inductive bias show a significant performance boost due to advanced network-level and block-level designs, but performance differences persist among different STMs.
SSN: Learning Sparse Switchable Normalization via SparsestMax
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation
In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric.
Point2Seq: Detecting 3D Objects as Sequences
We further propose a lightweight scene-to-sequence decoder that can auto-regressively generate words conditioned on features from a 3D scene as well as cues from the preceding words.
Decoupled Spatial-Temporal Transformer for Video Inpainting
Seamless combination of these two novel designs forms a better spatial-temporal attention scheme and our proposed model achieves better performance than state-of-the-art video inpainting approaches with significant boosted efficiency.
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
To tackle the challenges, we propose an end-to-end structured domain adaptation framework with an online relation-consistency regularization term.
Ranked #4 on
Unsupervised Domain Adaptation
on Market to MSMT
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
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
We propose a novel algorithm, named Open-Edit, which is the first attempt on open-domain image manipulation with open-vocabulary instructions.
Learning Degradation Representations for Image Deblurring
With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively.
Ranked #13 on
Image Deblurring
on GoPro
Neural Network Encapsulation
Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers.
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Therefore, to generate the images with preferred objects and rich interactions, we propose a semi-parametric method, PasteGAN, for generating the image from the scene graph and the image crops, where spatial arrangements of the objects and their pair-wise relationships are defined by the scene graph and the object appearances are determined by the given object crops.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
ViTAS: Vision Transformer Architecture Search
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling
The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans.
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering
Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering.
Weakly Supervised Contrastive Learning
Specifically, our proposed framework is based on two projection heads, one of which will perform the regular instance discrimination task.
Siamese Image Modeling for Self-Supervised Vision Representation Learning
Driven by these analysis, we propose Siamese Image Modeling (SiameseIM), which predicts the dense representations of an augmented view, based on another masked view from the same image but with different augmentations.
Dynamic Token Normalization Improves Vision Transformers
It is difficult for Transformers to capture inductive bias such as the positional context in an image with LN.
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)
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
A key observation is that it is difficult to classify anchors of different sizes with the same set of features.
Ranked #2 on
Region Proposal
on COCO test-dev
Zoom Out-and-In Network with Recursive Training for Object Proposal
In this paper, we propose a zoom-out-and-in network for generating object proposals.
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
In this CC-Net, the cascaded classifier at a stage is aided by the classification scores in previous stages.
Cascaded Refinement Network for Point Cloud Completion
Point clouds are often sparse and incomplete.
Cascaded Refinement Network for Point Cloud Completion with Self-supervision
This is to mitigate the dependence of existing approaches on large amounts of ground truth training data that are often difficult to obtain in real-world applications.
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space
It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive.
Ranked #2 on
Transferability
on classification benchmark
Channel Equilibrium Networks for Learning Deep Representation
Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.
Real-time Controllable Denoising for Image and Video
Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness.
Voxel-based Network for Shape Completion by Leveraging Edge Generation
Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs.
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.
Ranked #1 on
3D Semantic Scene Completion
on NYUv2
Cached Transformers: Improving Transformers with Differentiable Memory Cache
This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens.
Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize
Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth.
CoNe: Contrast Your Neighbours for Supervised Image Classification
Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution.
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels.
Point Cloud Completion by Learning Shape Priors
Then we learn a mapping to transfer the point features from partial points to that of the complete points by optimizing feature alignment losses.
Adaptive Momentum Coefficient for Neural Network Optimization
We propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term.
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking
In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting.
Lehmer Transform and its Theoretical Properties
We propose a new class of transforms that we call {\it Lehmer Transform} which is motivated by the {\it Lehmer mean function}.
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Depth estimation and scene parsing are two particularly important tasks in visual scene understanding.
Ranked #15 on
Depth Estimation
on NYU-Depth V2
Learnable Histogram: Statistical Context Features for Deep Neural Networks
Statistical features, such as histogram, Bag-of-Words (BoW) and Fisher Vector, were commonly used with hand-crafted features in conventional classification methods, but attract less attention since the popularity of deep learning methods.
3D Human Pose Estimation in the Wild by Adversarial Learning
Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Exploring Disentangled Feature Representation Beyond Face Identification
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification.
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification
As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence.
Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data
The aim of image captioning is to generate captions by machine to describe image contents.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.
Spontaneous Symmetry Breaking in Neural Networks
We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory.
Visual Question Generation as Dual Task of Visual Question Answering
Recently visual question answering (VQA) and visual question generation (VQG) are two trending topics in the computer vision, which have been explored separately.
Optimization assisted MCMC
The global optimization method first reduces a high dimensional search to an one dimensional geodesic to find a starting point close to a local mode.
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
The visibility map of the target is learned and used for inferring the spatial attention map.
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals
Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation.
Identity-Aware Textual-Visual Matching with Latent Co-attention
The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism.
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions.
Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision
The experiments show that our proposed method makes deep models learn more discriminative feature representations without increasing model size or complexity.
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
In this paper, each visual relationship is considered as a phrase with three components.
Progressively Diffused Networks for Semantic Image Segmentation
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.
Automatic Discoveries of Physical and Semantic Concepts via Association Priors of Neuron Groups
The recent successful deep neural networks are largely trained in a supervised manner.
CRF-CNN: Modeling Structured Information in Human Pose Estimation
In a classical neural network, there is no message passing between neurons in the same layer.
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV).
Factors in Finetuning Deep Model for object detection
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
Multi-Bias Non-linear Activation in Deep Neural Networks
It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers.
Exemplar-AMMs: Recognizing Crowd Movements from Pedestrian Trajectories
Specifically, we propose an optimization framework that filters out the unknown noise in the crowd trajectories and measures their similarity to the exemplar-AMMs to produce a crowd motion feature.
Structured Feature Learning for Pose Estimation
In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation.
Window-Object Relationship Guided Representation Learning for Generic Object Detections
We propose a representation learning pipeline to use the relationship as supervision for improving the learned representation in object detection.
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.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification
The proposed algorithms eliminate all the redundant computation in convolution and pooling on images by introducing novel d-regularly sparse kernels.
Person Re-identification by Saliency Learning
(3) saliency matching is proposed based on patch matching.
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
Fully Convolutional Neural Networks for Crowd Segmentation
Based on FCNN, a multi-stage deep learning is proposed to integrate appearance and motion cues for crowd segmentation.
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.
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.
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.
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
We show that by encouraging deep message propagation and interactions between local object features and global predicate features, one can achieve compelling performance in recognizing complex relationships without using any linguistic priors.
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
Person Re-identification with Deep Similarity-Guided Graph Neural Network
However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs.
Ranked #2 on
Person Re-Identification
on CUHK03
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association
Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities.
Ranked #22 on
Text based Person Retrieval
on CUHK-PEDES
Question-Guided Hybrid Convolution for Visual Question Answering
Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features. To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage.
Ranked #14 on
Visual Question Answering (VQA)
on CLEVR
HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
Dense depth cues are important and have wide applications in various computer vision tasks.
Ranked #10 on
Depth Completion
on KITTI Depth Completion
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
Learning to Group and Label Fine-Grained Shape Components
We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components.
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering.
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.
Spatial Latent Dirichlet Allocation
In this paper, we propose a topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structure among visual words that are essential for solving many vision problems.
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.
Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding
The attention weights are obtained based on a query feature, which is learned from the whole probe snippet by an LSTM network, making the resulting embeddings less affected by noisy frames.
Ranked #4 on
Person Re-Identification
on PRID2011
Eliminating Background-Bias for Robust Person Re-Identification
State-of-the-art methods mainly utilize deep learning based approaches for learning visual features for describing person appearances.
Group Consistent Similarity Learning via Deep CRF for Person Re-Identification
Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities.
Spontaneous Symmetry Breaking in Deep Neural Networks
In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results.
Decoupling the Layers in Residual Networks
We propose a Warped Residual Network (WarpNet) using a parallelizable warp operator for forward and backward propagation to distant layers that trains faster than the original residual neural network.
Measuring Crowd Collectiveness
Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields.
Modeling Mutual Visibility Relationship in Pedestrian Detection
In this paper, we propose a mutual visibility deep model that jointly estimates the visibility statuses of overlapping pedestrians.
Single-Pedestrian Detection Aided by Multi-pedestrian Detection
A probabilistic framework is proposed to model the relationship between the configurations estimated by singleand multi-pedestrian detectors, and to refine the single-pedestrian detection result with multi-pedestrian detection.
Unsupervised Salience Learning for Person Re-identification
In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning.
Locally Aligned Feature Transforms across Views
In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person reidentification.
Deep Convolutional Network Cascade for Facial Point Detection
At each level, the outputs of multiple networks are fused for robust and accurate estimation.
Learning Mid-level Filters for Person Re-identification
In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification.
DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification
In this paper, we propose a novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter.
Switchable Deep Network for Pedestrian Detection
In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection.
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.
L0 Regularized Stationary Time Estimation for Crowd Group Analysis
We tackle stationary crowd analysis in this paper, which is similarly important as modeling mobile groups in crowd scenes and finds many applications in surveillance.
Multi-source Deep Learning for Human Pose Estimation
Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation.
Cross-Scene Crowd Counting via Deep Convolutional Neural Networks
To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count.
Ranked #15 on
Crowd Counting
on WorldExpo’10
Saliency Detection by Multi-Context Deep Learning
Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance.
Learning From Massive Noisy Labeled Data for Image Classification
To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels.
Understanding Pedestrian Behaviors From Stationary Crowd Groups
Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance.
Deeply Learned Attributes for Crowded Scene Understanding
We further measure user study performance on WWW and compare this with the proposed deep models.
Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution
Our analysis and empirical results show that classes with more samples have higher impact on the feature learning.
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.
STCT: Sequentially Training Convolutional Networks for Visual Tracking
To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features.
End-To-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation
In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts.
Slicing Convolutional Neural Network for Crowd Video Understanding
Learning and capturing both appearance and dynamic representations are pivotal for crowd video understanding.
Learning Object Interactions and Descriptions for Semantic Image Segmentation
This work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset.
Video Matting via Sparse and Low-Rank Representation
We introduce a novel method of video matting via sparse and low-rank representation.
Learning Deep Representation With Large-Scale Attributes
Experimental results show that the attributes are helpful in learning better features and improving the object detection accuracy by 2. 6% in mAP on the ILSVRC 2014 object detection dataset and 2. 4% in mAP on PASCAL VOC 2007 object detection dataset.
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.
Visual Tracking With Fully Convolutional Networks
Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet.
