Search Results for author:
Found 125 papers, 79 papers with code
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image.
JudgeLM: Fine-tuned Large Language Models are Scalable Judges
To address this problem, we propose to fine-tune LLMs as scalable judges (JudgeLM) to evaluate LLMs efficiently and effectively in open-ended benchmarks.
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Representing and rendering dynamic scenes has been an important but challenging task.
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
In recent times, the generation of 3D assets from text prompts has shown impressive results.
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models.
TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction
Additionally, 2D and 3D DSA imaging results can be generated from the reconstructed 4D DSA images.
TouchStone: Evaluating Vision-Language Models by Language Models
Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs).
Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition
Graph convolutional networks have been widely applied in skeleton-based gait recognition.
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
We propose a unified permutation-equivalent modeling approach, \ie, modeling map element as a point set with a group of equivalent permutations, which accurately describes the shape of map element and stabilizes the learning process.
Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
`3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving, aiming to predict voxel occupancy within volumetric scenes.
Ranked #1 on
3D Semantic Scene Completion
on KITTI-360
3D Semantic Scene Completion from a single RGB image
Autonomous Driving
ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration
We then leverage degradation-aware visual prompts to establish a controllable and universal model for image restoration, called ProRes, which is applicable to an extensive range of image restoration tasks.
Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification
Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is extensively employed in computer-assisted diagnosis (CAD).
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
Ranked #1 on
Multi-Object Tracking
on MOT20
(using extra training data)
Matte Anything: Interactive Natural Image Matting with Segment Anything Models
We leverage task-specific vision models to enhance the performance of natural image matting.
GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans.
ViTMatte: Boosting Image Matting with Pretrained Plain Vision Transformers
Recently, plain vision Transformers (ViTs) have shown impressive performance on various computer vision tasks, thanks to their strong modeling capacity and large-scale pretraining.
Ranked #1 on
Image Matting
on Distinctions-646
ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
In this work, we explore a scalable way for building a general representation model toward unlimited modalities.
Ranked #1 on
Audio-Visual Question Answering (AVQA)
on AVQA
(using extra training data)
A Range-Null Space Decomposition Approach for Fast and Flexible Spectral Compressive Imaging
We present RND-SCI, a novel framework for compressive hyperspectral image (HSI) reconstruction.
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
PTQ optimizes the quantization parameters by different metrics to minimize the perturbation of quantization.
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
We design a divide-and-conquer annotation scheme to solve the spatial extensibility problem of HD map generation, and abstract map elements with a variety of geometric patterns as unified point sequence representation, which can be extended to most map elements in the driving scene.
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightweight generic detectors.
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
MobileInst: Video Instance Segmentation on the Mobile
To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.
OpenInst: A Simple Query-Based Method for Open-World Instance Segmentation
Fortunately, we have identified two observations that help us achieve the best of both worlds: 1) query-based methods demonstrate superiority over dense proposal-based methods in open-world instance segmentation, and 2) learning localization cues is sufficient for open world instance segmentation.
Generalizable Neural Voxels for Fast Human Radiance Fields
Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem.
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
EVA-02: A Visual Representation for Neon Genesis
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
We present a path-based online lane graph construction method, termed LaneGAP, which end-to-end learns the path and recovers the lane graph via a Path2Graph algorithm.
Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt
Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels.
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts.
Graph Contrastive Learning for Skeleton-based Action Recognition
In this paper, we propose a graph contrastive learning framework for skeleton-based action recognition (\textit{SkeletonGCL}) to explore the \textit{global} context across all sequences.
Ranked #6 on
Skeleton Based Action Recognition
on NTU RGB+D
A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction
We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design with an adaptive alternate optimization framework for hyperspectral image (HSI) reconstruction.
RILS: Masked Visual Reconstruction in Language Semantic Space
Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training.
Query6DoF: Learning Sparse Queries as Implicit Shape Prior for Category-Level 6DoF Pose Estimation
Category-level 6DoF object pose estimation intends to estimate the rotation, translation, and size of unseen objects.
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
It determines the quantization parameters by using the information of differences between network prediction before and after quantization.
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.
Ranked #1 on
Object Detection
on COCO-O
(using extra training data)
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes.
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction
High-definition (HD) map provides abundant and precise environmental information of the driving scene, serving as a fundamental and indispensable component for planning in autonomous driving system.
Ranked #5 on
3D Lane Detection
on OpenLane-V2 val
Robust Multi-Object Tracking by Marginal Inference
To address the problem, we present an efficient approach to compute a marginal probability for each pair of objects in real time.
AiATrack: Attention in Attention for Transformer Visual Tracking
However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement.
Ranked #1 on
Visual Object Tracking
on NeedForSpeed
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Polar Parametrization for Vision-based Surround-View 3D Detection
Based on Polar Parametrization, we propose a surround-view 3D DEtection TRansformer, named PolarDETR.
Featurized Query R-CNN
The query mechanism introduced in the DETR method is changing the paradigm of object detection and recently there are many query-based methods have obtained strong object detection performance.
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer
GKT leverages the geometric priors to guide the transformer to focus on discriminative regions and unfolds kernel features to generate BEV representation.
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions.
Temporally Efficient Vision Transformer for Video Instance Segmentation
To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).
Ranked #27 on
Video Instance Segmentation
on OVIS validation
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semantic segmentation on mobile devices.
Multi-scale Context-aware Network with Transformer for Gait Recognition
Although gait recognition has drawn increasing research attention recently, since the silhouette differences are quite subtle in spatial domain, temporal feature representation is crucial for gait recognition.
Ranked #1 on
Multiview Gait Recognition
on CASIA-B
Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection
We present an approach to efficiently and effectively adapt a masked image modeling (MIM) pre-trained vanilla Vision Transformer (ViT) for object detection, which is based on our two novel observations: (i) A MIM pre-trained vanilla ViT encoder can work surprisingly well in the challenging object-level recognition scenario even with randomly sampled partial observations, e. g., only 25% $\sim$ 50% of the input embeddings.
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception
For segmentation, we integrate AziNorm into KPConv.
Sparse Instance Activation for Real-Time Instance Segmentation
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation.
Ranked #8 on
Real-time Instance Segmentation
on MSCOCO
Corrupted Image Modeling for Self-Supervised Visual Pre-Training
Given this corrupted image, an enhancer network learns to either recover all the original image pixels, or predict whether each visual token is replaced by a generator sample or not.
NeuSample: Neural Sample Field for Efficient View Synthesis
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.
Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge
To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario.
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Ranked #1 on
Multiple Object Tracking
on BDD100K val
YOLOP: You Only Look Once for Panoptic Driving Perception
A panoptic driving perception system is an essential part of autonomous driving.
Ranked #2 on
Drivable Area Detection
on BDD100K val
VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild
We estimate 3D poses from the voxel representation by predicting whether each voxel contains a particular body joint.
Ranked #7 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
Hierarchical Aggregation for 3D Instance Segmentation
Instance segmentation on point clouds is a fundamental task in 3D scene perception.
Ranked #4 on
3D Instance Segmentation
on S3DIS
(mCov metric, using extra
training data)
What Makes for Hierarchical Vision Transformer?
Recent studies indicate that hierarchical Vision Transformer with a macro architecture of interleaved non-overlapped window-based self-attention \& shifted-window operation is able to achieve state-of-the-art performance in various visual recognition tasks, and challenges the ubiquitous convolutional neural networks (CNNs) using densely slid kernels.
Bag of Instances Aggregation Boosts Self-supervised Distillation
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
Tracking Instances as Queries
Recently, query based deep networks catch lots of attention owing to their end-to-end pipeline and competitive results on several fundamental computer vision tasks, such as object detection, semantic segmentation, and instance segmentation.
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure?
Ranked #31 on
Object Detection
on COCO-O
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Transformers have offered a new methodology of designing neural networks for visual recognition.
Instances as Queries
The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage.
Ranked #13 on
Object Detection
on COCO-O
(using extra training data)
Crossover Learning for Fast Online Video Instance Segmentation
For temporal information modeling in VIS, we present a novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames.
Ranked #26 on
Video Instance Segmentation
on OVIS validation
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images
Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based solution for weakly-supervised instance segmentation.
Ranked #5 on
Box-supervised Instance Segmentation
on COCO test-dev
(using extra training data)
Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation
Despite their success for semantic segmentation, convolutional neural networks are ill-equipped for incremental learning, \ie, adapting the original segmentation model as new classes are available but the initial training data is not retained.
Class-Incremental Semantic Segmentation
Incremental Learning
+1
Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation
Based on deep networks, video object detection is actively studied for pushing the limits of detection speed and accuracy.
Human De-occlusion: Invisible Perception and Recovery for Humans
Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion.
Noise Modulation: Let Your Model Interpret Itself
Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it, the interpretability of these models becomes an important issue. The majority of previous research works on the post-hoc interpretation of a trained model. But recently, adversarial training shows that it is possible for a model to have an interpretable input-gradient through training. However, adversarial training lacks efficiency for interpretability. To resolve this problem, we construct an approximation of the adversarial perturbations and discover a connection between adversarial training and amplitude modulation.
Deep Online Correction for Monocular Visual Odometry
Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases.
Occluded Video Instance Segmentation: A Benchmark
On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.
Ranked #30 on
Video Instance Segmentation
on OVIS validation
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition
By applying the proposed feature matching block in different layers of the few-shot recognition network, multi-scale information among the compared images can be incorporated into the final cascaded matching feature, which boosts the recognition performance further and generalizes better by learning on relationships.
ResizeMix: Mixing Data with Preserved Object Information and True Labels
Especially, CutMix uses a simple but effective method to improve the classifiers by randomly cropping a patch from one image and pasting it on another image.
EfficientPose: Efficient Human Pose Estimation with Neural Architecture Search
Human pose estimation from image and video is a vital task in many multimedia applications.
Medial Injury/Dysfunction Induced Granulation Tissue Repair is the Pathogenesis of Atherosclerosis
Myofibroblasts, ECM and lumen (intima)/vasa vasorum (VV) (adventitia) constitute granulation tissue repair.
Hemodynamic Bigger Hydrostatic Pressure Instead of Lower Shear Stress Aggravates Atherosclerosis
When blood micro cluster flows over a very short distance or the same transection of the artery, previous studies did not consider the conversion between 1/2\r{ho}v^2 and P. Therefore, low shear stress aggravates atherosclerosis is an appearance, and the essence is that these areas with smaller blood velocity have much bigger hydrostatic pressure, which aggravates atherosclerosis.
Myofibroblast Forms Atherosclerotic Plaques
It is the first time that lipid rich plaques with lots of foam cells, extracellular lipids and collagen fibers formed in vitro.
Deep multi-metric learning for text-independent speaker verification
Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services.
Boundary-preserving Mask R-CNN
Besides, it is not surprising to observe that BMask R-CNN obtains more obvious improvement when the evaluation criterion requires better localization (e. g., AP$_{75}$) as shown in Fig. 1.
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e. g. an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Ranked #3 on
Multi-Object Tracking
on HiEve
Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label
Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training.
AlignSeg: Feature-Aligned Segmentation Networks
Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation.
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
In our experiments, we conduct FNA on MobileNetV2 to obtain new networks for both segmentation and detection that clearly out-perform existing networks designed both manually and by NAS.
Diversity Transfer Network for Few-Shot Learning
The learning problem of the sample generation (i. e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works.
IoU-aware Single-stage Object Detector for Accurate Localization
The detection confidence is then used as the input of the subsequent NMS and COCO AP computation, which will substantially improve the localization accuracy of models.
Deep High-Resolution Representation Learning for Visual Recognition
High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
IoU-balanced Loss Functions for Single-stage Object Detection
The IoU-balanced localization loss decreases the gradient of examples with low IoU and increases the gradient of examples with high IoU, which can improve the localization accuracy of models.
Object Detection in Video with Spatial-temporal Context Aggregation
Without any bells and whistles, our method obtains 80. 3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts.
Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation
Video object segmentation (VOS) aims at pixel-level object tracking given only the annotations in the first frame.
Ranked #1 on
Visual Object Tracking
on YouTube-VOS 2018
(Jaccard (Seen) metric)
Densely Connected Search Space for More Flexible Neural Architecture Search
We revisit the search space design in most previous NAS methods and find the number and widths of blocks are set manually.
Ranked #91 on
Neural Architecture Search
on ImageNet
High-Resolution Representations for Labeling Pixels and Regions
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
Ranked #5 on
Semantic Segmentation
on LIP val
Reducing the dilution: An analysis of the information sensitiveness of capsule network with a practical improvement method
Capsule network has shown various advantages over convolutional neural network (CNN).
Direct Object Recognition Without Line-of-Sight Using Optical Coherence
Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications.
Mask Scoring R-CNN
In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.
Ranked #72 on
Instance Segmentation
on COCO test-dev
EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search
In our implementations, architectures are first searched on a small dataset, e. g., CIFAR-10.
CCNet: Criss-Cross Attention for Semantic Segmentation
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #7 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Detect or Track: Towards Cost-Effective Video Object Detection/Tracking
Trackers are in general more efficient than detectors but bear the risk of drifting.
Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification
We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation.
Weakly Supervised Region Proposal Network and Object Detection
The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.
Reinforced Evolutionary Neural Architecture Search
To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE- NAS), which is an evolutionary method with the reinforced mutation for NAS.
Unsupervised Domain Adaptive Re-Identification: Theory and Practice
We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.
Ranked #14 on
Unsupervised Domain Adaptation
on Market to Duke
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
Ranked #2 on
Weakly Supervised Object Detection
on HICO-DET
ASTER: An Attentional Scene Text Recognizer with Flexible Rectification
SCENE text recognition has attracted great interest from the academia and the industry in recent years owing to its importance in a wide range of applications.
Ranked #20 on
Scene Text Recognition
on ICDAR2015
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Weakly-Supervised Semantic Segmentation Network With Deep Seeded Region Growing
Inspired by the traditional image segmentation methods of seeded region growing, we propose to train a semantic segmentation network starting from the discriminative regions and progressively increase the pixel-level supervision using by seeded region growing.
Ranked #30 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
Object Detection in Videos by High Quality Object Linking
In particular, our method improves results by 8. 8% over the static image detector for fast moving objects.
Point Linking Network for Object Detection
The deep ConvNets based object detectors mainly focus on regressing the coordinates of bounding box, e. g., Faster-R-CNN, YOLO and SSD.
Deep Patch Learning for Weakly Supervised Object Classification and Discovery
Patch-level image representation is very important for object classification and detection, since it is robust to spatial transformation, scale variation, and cluttered background.
Multiple Instance Detection Network with Online Instance Classifier Refinement
We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i. e., without object location information.
TextBoxes: A Fast Text Detector with a Single Deep Neural Network
This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression.
Revisiting Multiple Instance Neural Networks
We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label.
Deep FisherNet for Object Classification
Our proposed FisherNet combines convolutional neural network training and Fisher Vector encoding in a single end-to-end structure.
Shape Recognition by Bag of Skeleton-associated Contour Parts
Contour and skeleton are two complementary representations for shape recognition.
Robust Scene Text Recognition with Automatic Rectification
We show that the model is able to recognize several types of irregular text, including perspective text and curved text.
Ranked #10 on
Scene Text Recognition
on ICDAR 2003
Bag Reference Vector for Multi-instance Learning
Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics.
Relaxed Multiple-Instance SVM with Application to Object Discovery
Multiple-instance learning (MIL) has served as an important tool for a wide range of vision applications, for instance, image classification, object detection, and visual tracking.
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection
Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition.
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
By combing the global deep learning representation and the local descriptor representation, our method can obtain the state-of-the-art performance on 3D shape retrieval benchmarks.
Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning
We propose a novel inference framework for finding maximal cliques in a weighted graph that satisfy hard constraints.
