Search Results for author:
Found 120 papers, 71 papers with code
Instance-Aware Embedding for Point Cloud Instance Segmentation
However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects.
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates.
DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
We achieve results on par with SAM-vit-h using only 0. 06% of their data (e. g., 600K vs. 1B pixel-level annotated images).
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction
3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction.
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
We curate a benchmark comprising 22 multimodal datasets from diverse real-world applications, encompassing all 4 combinations of the 3 modalities.
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
We introduce InternVL 2. 5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2. 0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality.
Ranked #1 on
Video Question Answering
on NExT-QA
Two-Stage Adaptive Robust Optimization Model for Joint Unit Maintenance and Unit Commitment Considering Source-Load Uncertainty
Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance.
Factorized Visual Tokenization and Generation
Visual tokenizers are fundamental to image generation.
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild
To tackle these challenges, we propose a dynamic-aware tracking any point (DATAP) method that leverages consistent video depth and point tracking.
DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion
Then, we propose a novel text-driven panoramic generation framework, termed DiffPano, to achieve scalable, consistent, and diverse panoramic scene generation.
EMMA: End-to-End Multimodal Model for Autonomous Driving
We show that co-training EMMA with planner trajectories, object detection, and road graph tasks yields improvements across all three domains, highlighting EMMA's potential as a generalist model for autonomous driving applications.
Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction
Our model simultaneously estimates the camera pose from a single image and predicts the view from a new camera pose, effectively bridging the gap between spatial awareness and visual prediction.
Depth Any Video with Scalable Synthetic Data
Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results.
VideoSAM: Open-World Video Segmentation
Video segmentation is essential for advancing robotics and autonomous driving, particularly in open-world settings where continuous perception and object association across video frames are critical.
SPA: 3D Spatial-Awareness Enables Effective Embodied Representation
The results are compelling: SPA consistently outperforms more than 10 state-of-the-art representation methods, including those specifically designed for embodied AI, vision-centric tasks, and multi-modal applications, while using less training data.
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos.
GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction
To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation
Recent advancements in generative models have significantly enhanced their capacity for image generation, enabling a wide range of applications such as image editing, completion and video editing.
DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting
Extensive experiments demonstrate that DynaSurfGS surpasses state-of-the-art methods in both high-fidelity surface reconstruction and photorealistic rendering.
ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction
Neural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces.
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction.
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliability of measurements.
GQE: Generalized Query Expansion for Enhanced Text-Video Retrieval
This paper introduces a novel data-centric approach, Generalized Query Expansion (GQE), to address the inherent information imbalance between text and video, enhancing the effectiveness of text-video retrieval systems.
Unified Lexical Representation for Interpretable Visual-Language Alignment
On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation and interpretable, providing exact matches for individual words.
Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation
In this technical report, we detail our first-place solution for the 2024 Waymo Open Dataset Challenge's semantic segmentation track.
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
To fully exploit rich information hidden in long-term temporal point clouds and images, we present the Temporal Aggregation Network, termed TASeg.
New Desiderata for Direct Preference Optimization
Large language models in the past have typically relied on some form of reinforcement learning with human feedback (RLHF) to better align model responses with human preferences.
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of spatio-temporal prediction networks.
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
In this paper, we introduce a novel path to $\textit{general}$ human motion generation by focusing on 2D space.
MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers
Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement.
Adaptive Slot Attention: Object Discovery with Dynamic Slot Number
Moreover, our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity, offering the potential for further exploration in slot attention research.
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Parameter-efficient fine-tuning (PEFT) has become increasingly important as foundation models continue to grow in both popularity and size.
EMR-Merging: Tuning-Free High-Performance Model Merging
In this paper, we rethink and analyze the existing model merging paradigm.
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection
3D object detectors for point clouds often rely on a pooling-based PointNet to encode sparse points into grid-like voxels or pillars.
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
Hallucination of Multimodal Large Language Models: A Survey
By drawing the granular classification and landscapes of hallucination causes, evaluation benchmarks, and mitigation methods, this survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
GVGEN: Text-to-3D Generation with Volumetric Representation
To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization.
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini.
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence.
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end.
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
These outcomes suggest that the 3D point cloud is a valuable observation modality for intricate robotic tasks.
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation.
CaMML: Context-Aware Multimodal Learner for Large Models
In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs).
Ranked #141 on
Visual Question Answering
on MM-Vet
Point Transformer V3: Simpler Faster Stronger
This paper is not motivated to seek innovation within the attention mechanism.
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
We first validate eight manually-defined partial fine-tuning strategies across kinds of datasets and vision transformer architectures, and find that some partial fine-tuning strategies (e. g., ffn only or attention only) can achieve better performance with fewer tuned parameters than full fine-tuning, and selecting appropriate layers is critical to partial fine-tuning.
Point Transformer V3: Simpler, Faster, Stronger
This paper is not motivated to seek innovation within the attention mechanism.
Ranked #1 on
3D Semantic Segmentation
on ScanNet++
(using extra training data)
DreamComposer: Controllable 3D Object Generation via Multi-View Conditions
However, due to the lack of information from multiple views, these works encounter difficulties in generating controllable novel views.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
Ranked #1 on
Pedestrian Image Caption
on CUHK-PEDES
Consistent Video-to-Video Transfer Using Synthetic Dataset
We introduce a novel and efficient approach for text-based video-to-video editing that eliminates the need for resource-intensive per-video-per-model finetuning.
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning.
Compatible Transformer for Irregularly Sampled Multivariate Time Series
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged.
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged.
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
Ranked #2 on
Semantic Segmentation
on S3DIS
(using extra training data)
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.
Coarse-to-Fine Amodal Segmentation with Shape Prior
To address this issue, we propose a convolution refine module to inject fine-grained information and provide a more precise amodal object segmentation based on visual features and coarse-predicted segmentation.
Boosting Residual Networks with Group Knowledge
Specifically, we implicitly divide all subnets into hierarchical groups by subnet-in-subnet sampling, aggregate the knowledge of different subnets in each group during training, and exploit upper-level group knowledge to supervise lower-level subnet groups.
Experts Weights Averaging: A New General Training Scheme for Vision Transformers
As Vision Transformers (ViTs) are gradually surpassing CNNs in various visual tasks, one may question: if a training scheme specifically for ViTs exists that can also achieve performance improvement without increasing inference cost?
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
To address this issue, we propose a novel diffusion-based feature learning framework that explores Multi-Timestep Multi-Stage Diffusion features for HSI classification for the first time, called MTMSD.
SAM3D: Segment Anything in 3D Scenes
In this work, we propose SAM3D, a novel framework that is able to predict masks in 3D point clouds by leveraging the Segment-Anything Model (SAM) in RGB images without further training or finetuning.
Learning for Transductive Threshold Calibration in Open-World Recognition
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true negative rates (TNR).
PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer
The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries.
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks
In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond their performance limits.
Learning Manifold Dimensions with Conditional Variational Autoencoders
Although the variational autoencoder (VAE) and its conditional extension (CVAE) are capable of state-of-the-art results across multiple domains, their precise behavior is still not fully understood, particularly in the context of data (like images) that lie on or near a low-dimensional manifold.
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts.
$β$-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search
To address the robustness problem, we first benchmark different NAS methods under a wide range of proxy data, proxy channels, proxy layers and proxy epochs, since the robustness of NAS under different kinds of proxies has not been explored before.
Crossing the Gap: Domain Generalization for Image Captioning
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
Ponder: Point Cloud Pre-training via Neural Rendering
We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering.
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection
Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training.
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas.
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder
These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation.
GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds
In contrast to previous 3D MAE frameworks, which either design a complex decoder to infer masked information from maintained regions or adopt sophisticated masking strategies, we instead propose a much simpler paradigm.
Reconstructing Hand-Held Objects from Monocular Video
The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker.
3D-QueryIS: A Query-based Framework for 3D Instance Segmentation
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness.
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Data augmentations are important in training high-performance 3D object detectors for point clouds.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
Long-tail distribution is widely spread in real-world applications.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively.
PointInst3D: Segmenting 3D Instances by Points
The current state-of-the-art methods in 3D instance segmentation typically involve a clustering step, despite the tendency towards heuristics, greedy algorithms, and a lack of robustness to the changes in data statistics.
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems.
Graph-Enhanced Exploration for Goal-oriented Reinforcement Learning
Goal-oriented Reinforcement Learning (GoRL) is a promising approach for scaling up RL techniques on sparse reward environments requiring long horizon planning.
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited
We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles.
Ranked #1 on
3D Object Reconstruction From A Single Image
on RenderPeople
(using extra training data)
3D Object Reconstruction From A Single Image
Image-to-Image Translation
Progressive Coordinate Transforms for Monocular 3D Object Detection
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment.
Dynamic Convolution for 3D Point Cloud Instance Segmentation
The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
Learning Hierarchical Graph Neural Networks for Image Clustering
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
Scene flow in 3D point clouds plays an important role in understanding dynamic environments.
ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting
Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output.
Ranked #7 on
Text Spotting
on Inverse-Text
Explore with Dynamic Map: Graph Structured Reinforcement Learning
In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.
DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution
Previous top-performing approaches for point cloud instance segmentation involve a bottom-up strategy, which often includes inefficient operations or complex pipelines, such as grouping over-segmented components, introducing additional steps for refining, or designing complicated loss functions.
Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction
We propose Geo-PIFu, a method to recover a 3D mesh from a monocular color image of a clothed person.
FCOS: A simple and strong anchor-free object detector
In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications.
Improving Semantic Segmentation via Self-Training
In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required to learn accurate models.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #10 on
Instance Segmentation
on COCO test-dev
(APM metric)
ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
Our contributions are three-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve.
Ranked #9 on
Text Spotting
on Inverse-Text
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation
3D point cloud semantic and instance segmentation is crucial and fundamental for 3D scene understanding.
Ranked #29 on
3D Instance Segmentation
on ScanNet(v2)
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints.
Ranked #46 on
Skeleton Based Action Recognition
on NTU RGB+D
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors.
Ranked #3 on
Scene Text Detection
on ICDAR 2017 MLT
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning
To learn this representation, we train a squeeze network to drive using annotations for the side task as input.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #34 on
Node Classification
on Cora
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating).
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
One difficulty we observe is that the noise in the stochastic momentum estimation is accumulated over time and will have delayed effects when the batch size changes.
FCOS: Fully Convolutional One-Stage Object Detection
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Ranked #4 on
Pedestrian Detection
on TJU-Ped-campus
Knowledge Adaptation for Efficient Semantic Segmentation
To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
Ranked #49 on
Semantic Segmentation
on PASCAL Context
Bag of Freebies for Training Object Detection Neural Networks
Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}.
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
We present a method to infer 3D pose and shape of vehicles from a single image.
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling.
Bag of Tricks for Image Classification with Convolutional Neural Networks
Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.
Ranked #38 on
Domain Generalization
on VizWiz-Classification
FashionNet: Personalized Outfit Recommendation with Deep Neural Network
Our system, dubbed FashionNet, consists of two components, a feature network for feature extraction and a matching network for compatibility computation.
An end-to-end TextSpotter with Explicit Alignment and Attention
This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances.
Single Shot Text Detector with Regional Attention
Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench- mark, advancing the state-of-the-art results in [18, 28].
Ranked #4 on
Scene Text Detection
on COCO-Text
Detecting Text in Natural Image with Connectionist Text Proposal Network
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image.
Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
We propose a novel Cascaded Convolutional Text Network (CCTN) that joints two customized convolutional networks for coarse-to-fine text localization.
Text-attentional convolutional neural network for scene text detection
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images.
Text-Attentional Convolutional Neural Networks for Scene Text Detection
The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components.
