Search Results for author:
Found 130 papers, 44 papers with code
Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-Order Feature Analysis
The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features and models.
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation
Online knowledge distillation has attracted increasing interest recently, which jointly learns teacher and student models or an ensemble of student models simultaneously and collaboratively.
MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models
However, the method of generating illumination maps has poor generalization performance and parametric models such as Spherical Harmonic (SH) and Spherical Gaussian (SG) fall short in capturing high-frequency or low-frequency components.
Efficient Test-Time Adaptation of Vision-Language Models
TDA works with a lightweight key-value cache that maintains a dynamic queue with few-shot pseudo labels as values and the corresponding test-sample features as keys.
Masked AutoDecoder is Effective Multi-Task Vision Generalist
Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction.
StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting
We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps).
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis.
Weakly Supervised Monocular 3D Detection with a Single-View Image
We propose SKD-WM3D, a weakly supervised monocular 3D detection framework that exploits depth information to achieve M3D with a single-view image exclusively without any 3D annotations or other training data.
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt
We design DivAvatar, a novel framework that generates diverse avatars, empowering 3D creatives with a multitude of distinct and richly varied 3D avatars from a single text prompt.
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors
This paper presents DVDet, a Descriptor-Enhanced Open Vocabulary Detector that introduces conditional context prompts and hierarchical textual descriptors that enable precise region-text alignment as well as open-vocabulary detection training in general.
Conditional Tuning Network for Few-Shot Adaptation of Segmentation Anything Model
CAT-SAM freezes the entire SAM and adapts its mask decoder and image encoder simultaneously with a small number of learnable parameters.
Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining
Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space.
Domain Adaptation for Large-Vocabulary Object Detectors
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data.
Learning to Prompt Segment Anything Models
SAMs work with two types of prompts including spatial prompts (e. g., points) and semantic prompts (e. g., texts), which work together to prompt SAMs to segment anything on downstream datasets.
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pre-defined and fixed model interface that has limited interactivity and adaptability in following user' task instructions.
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Large Vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned.
AI-Generated Images as Data Source: The Dawn of Synthetic Era
The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data.
Noise-Tolerant Unsupervised Adapter for Vision-Language Models
NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few-shot unlabelled target samples as key-value pairs.
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations.
Domain Generalization via Balancing Training Difficulty and Model Capability
Despite its recent progress, most existing work suffers from the misalignment between the difficulty level of training samples and the capability of contemporarily trained models, leading to over-fitting or under-fitting in the trained generalization model.
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization.
Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin Memory
However, the source predictions of target data are often noisy and training with them is prone to learning collapses.
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation.
One-Shot Action Recognition via Multi-Scale Spatial-Temporal Skeleton Matching
The first is multi-scale matching which captures the scale-wise semantic relevance of skeleton data at multiple spatial and temporal scales simultaneously.
Prompt Ensemble Self-training for Open-Vocabulary Domain Adaptation
Traditional domain adaptation assumes the same vocabulary across source and target domains, which often struggles with limited transfer flexibility and efficiency while handling target domains with different vocabularies.
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the progress achieved in this area.
Weakly Supervised 3D Open-vocabulary Segmentation
Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research.
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence.
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization
We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data.
POCE: Pose-Controllable Expression Editing
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
Face Transformer: Towards High Fidelity and Accurate Face Swapping
Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces.
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively.
Vision-Language Models for Vision Tasks: A Survey
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laborious and time-consuming visual recognition paradigm.
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
Modeling Continuous Motion for 3D Point Cloud Object Tracking
The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.
Regularized Vector Quantization for Tokenized Image Synthesis
The first is a prior distribution regularization which measures the discrepancy between a prior token distribution and the predicted token distribution to avoid codebook collapse and low codebook utilization.
Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger
Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention
3D object detection with surround-view images is an essential task for autonomous driving.
Domain Adaptive Scene Text Detection via Subcategorization
We study domain adaptive scene text detection, a largely neglected yet very meaningful task that aims for optimal transfer of labelled scene text images while handling unlabelled images in various new domains.
Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors
Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors.
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.
TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics.
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution
In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes.
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion
The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection.
Contextual Text Block Detection towards Scene Text Understanding
We formulate the new setup by a dual detection task which first detects integral text units and then groups them into a CTB.
Auto-regressive Image Synthesis with Integrated Quantization
Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
Towards Counterfactual Image Manipulation via CLIP
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
Specifically, we design cross-frame pseudo labelling to provide pseudo supervision from previous video frames while learning from the augmented current video frames.
VMRF: View Matching Neural Radiance Fields
With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration
Domain adaptive panoptic segmentation aims to mitigate data annotation challenge by leveraging off-the-shelf annotated data in one or multiple related source domains.
Ranked #2 on
Domain Adaptation
on Panoptic SYNTHIA-to-Cityscapes
Marginal Contrastive Correspondence for Guided Image Generation
We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.
Fourier Document Restoration for Robust Document Dewarping and Recognition
State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth.
Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation
We build the balanced subclass distributions by clustering pixels of each original class into multiple subclasses of similar sizes, which provide class-balanced pseudo supervision to regularize the class-biased segmentation.
Modulated Contrast for Versatile Image Synthesis
Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.
Accelerating DETR Convergence via Semantic-Aligned Matching
First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics.
Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting
Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features, respectively, as well as a visual-textual decoder that models the interaction among textual and visual features for learning effective scene text representations.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data.
Dual Learning Music Composition and Dance Choreography
Music and dance have always co-existed as pillars of human activities, contributing immensely to the cultural, social, and entertainment functions in virtually all societies.
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
One aspect that has been obviated so far, is the fact that how we represent the skeletal pose has a critical impact on the prediction results.
Multimodal Image Synthesis and Editing: The Generative AI Era
With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.
Music-to-Dance Generation with Optimal Transport
We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music.
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
To this end, we design an innovative historical contrastive learning (HCL) technique that exploits historical source hypothesis to make up for the absence of source data in UMA.
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training.
Contextual Text Detection
This paper presents Contextual Text Detection, a new setup that detects contextual text blocks for better understanding of texts in scenes.
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
We investigate unsupervised representation learning for skeleton action recognition, and design a novel skeleton cloud colorization technique that is capable of learning skeleton representations from unlabeled skeleton sequence data.
WaveFill: A Wavelet-based Generation Network for Image Inpainting
This paper presents WaveFill, a wavelet-based inpainting network that decomposes images into multiple frequency bands and fills the missing regions in each frequency band separately and explicitly.
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) for consecutive frames of target-domain videos.
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world.
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis.
Bi-level Feature Alignment for Versatile Image Translation and Manipulation
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features.
Blind Image Super-Resolution via Contrastive Representation Learning
Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.
Sparse Needlets for Lighting Estimation with Spherical Transport Loss
Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.
Unbalanced Feature Transport for Exemplar-based Image Translation
In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully.
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains.
Classification
Multi-Source Unsupervised Domain Adaptation
+2
Spectral Unsupervised Domain Adaptation for Visual Recognition
Second, it introduces multi-view spectral learning that learns useful unsupervised representations by maximizing mutual information among multiple ST-generated spectral views of each target sample.
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
We position the few labeled target samples as references that gauge the similarity between source and target features and guide adaptive inter-domain alignment for learning more similar source features.
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks.
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
With FAA-generated samples, the training can continue the 'random walk' and drift into an area with a flat loss landscape, leading to more robust domain adaptation.
I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition
The first task focuses on image-to-character (I2C) mapping which detects a set of character candidates from images based on different alignments of visual features in an non-sequential way.
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers
With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions.
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion
DA-DETR introduces a novel CNN-Transformer Blender (CTBlender) that fuses the CNN features and Transformer features ingeniously for effective feature alignment and knowledge transfer across domains.
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation
Recent progresses in domain adaptive semantic segmentation demonstrate the effectiveness of adversarial learning (AL) in unsupervised domain adaptation.
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.
Ranked #7 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Cross-View Regularization for Domain Adaptive Panoptic Segmentation
The inter-task regularization exploits the complementary nature of instance segmentation and semantic segmentation and uses it as a constraint for better feature alignment across domains.
Ranked #2 on
Domain Adaptation
on Panoptic SYNTHIA-to-Mapillary
FSDR: Frequency Space Domain Randomization for Domain Generalization
It has been studied widely by domain randomization that transfers source images to different styles in spatial space for learning domain-agnostic features.
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation
Specifically, we design a residual dense block with multiple receptive fields as a building block in the encoder which preserves detailed information in each modality and learns hierarchical modality-specific and fused features effectively.
Ranked #23 on
3D Semantic Segmentation
on SemanticKITTI
Detection and Rectification of Arbitrary Shaped Scene Texts by using Text Keypoints and Links
Detection and recognition of scene texts of arbitrary shapes remain a grand challenge due to the super-rich text shape variation in text line orientations, lengths, curvatures, etc.
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
Specifically, we design an uncertainty metric that assesses the alignment of each sample and adjusts the strength of adversarial learning for well-aligned and poorly-aligned samples adaptively.
GMLight: Lighting Estimation via Geometric Distribution Approximation
This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.
EMLight: Lighting Estimation via Spherical Distribution Approximation
Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution.
Adversarial Image Composition with Auxiliary Illumination
State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected.
LEED: Label-Free Expression Editing via Disentanglement
Recent studies on facial expression editing have obtained very promising progress.
Towards Realistic 3D Embedding via View Alignment
Recent advances in generative adversarial networks (GANs) have achieved great success in automated image composition that generates new images by embedding interested foreground objects into background images automatically.
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations.
Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.
Domain Adaptive Person Re-Identification
Ensemble Learning
+1
A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification
RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities.
Cross-Modality Person Re-identification
Person Re-Identification
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown.
Ranked #8 on
Unsupervised Domain Adaptation
on Duke to Market
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty.
Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses
Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing.
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
Facial Expression Recognition
Facial Expression Recognition (FER)
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition
Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind.
Coupled-Projection Residual Network for MRI Super-Resolution
The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer.
Hierarchy Composition GAN for High-fidelity Image Synthesis
Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
Scene Text Synthesis for Efficient and Effective Deep Network Training
Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.
MSR: Multi-Scale Shape Regression for Scene Text Detection
State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes.
ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification
Automated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary variation of text appearances in perspective distortion, text line curvature, text styles and different types of imaging artifacts.
Spatial Fusion GAN for Image Synthesis
Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry space but few in both.
A pooling based scene text proposal technique for scene text reading in the wild
Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding.
Attention Driven Person Re-identification
Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.
Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping
This paper presents a scene text detection technique that exploits bootstrapping and text border semantics for accurate localization of texts in scenes.
Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
This paper presents a novel image synthesis technique that aims to generate a large amount of annotated scene text images for training accurate and robust scene text detection and recognition models.
WeText: Scene Text Detection under Weak Supervision
With a "light" supervised model trained on a small fully annotated dataset, we explore semi-supervised and weakly supervised learning on a large unannotated dataset and a large weakly annotated dataset, respectively.
TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal
Recently, the regression-based object detectors and long-term recurrent convolutional network (LRCN) have demonstrated superior performance in human action detection and recognition.
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
This report introduces RCTW, a new competition that focuses on Chinese text reading.
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks
In this paper, we present YoTube-a novel network fusion framework for searching action proposals in untrimmed videos, where each action proposal corresponds to a spatialtemporal video tube that potentially locates one human action.
Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition
We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) - an improved attention-based architecture for multiple object recognition.
WordFence: Text Detection in Natural Images with Border Awareness
In recent years, text recognition has achieved remarkable success in recognizing scanned document text.
Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition
RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios.
Text Flow: A Unified Text Detection System in Natural Scene Images
With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively.
Diagnosing State-Of-The-Art Object Proposal Methods
Recently, Hosang et al. conduct the first unified study of existing methods' in terms of various image-level degradations.
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision.
