Search Results for author:
Found 56 papers, 23 papers with code
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.
Single-cell gene regulatory network analysis for mixed cell populations with applications to COVID-19 single cell data
In this paper, we consider inferring GRNs in single cells based on single cell RNA sequencing (scRNA-seq) data.
Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation
In two typical cross-domain semantic segmentation tasks, i. e., GTA5 to Cityscapes and SYNTHIA to Cityscapes, our method achieves the state-of-the-art segmentation accuracy.
Content-Variant Reference Image Quality Assessment via Knowledge Distillation
The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality.
Time-Aware Neighbor Sampling for Temporal Graph Networks
In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.
Directed Graph Contrastive Learning
However, it is still in its infancy with two concerns: 1) changing the graph structure through data augmentation to generate contrastive views may mislead the message passing scheme, as such graph changing action deprives the intrinsic graph structural information, especially the directional structure in directed graphs; 2) since GCL usually uses predefined contrastive views with hand-picking parameters, it does not take full advantage of the contrastive information provided by data augmentation, resulting in incomplete structure information for models learning.
Adaptive Data Augmentation on Temporal Graphs
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
Objects in Semantic Topology
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently.
Contextual Text Detection
This paper presents Contextual Text Detection, a new setup that detects contextual text blocks for better understanding of texts in scenes.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
Weakly Supervised Person Search with Region Siamese Networks
Large-scale labeled training data is often difficult to collect, especially for person identities.
Memory Based Video Scene Parsing
Video scene parsing is a long-standing challenging task in computer vision, aiming to assign pre-defined semantic labels to pixels of all frames in a given video.
Mining Contextual Information Beyond Image for Semantic Segmentation
To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations.
Cross-category Video Highlight Detection via Set-based Learning
For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework.
MT-ORL: Multi-Task Occlusion Relationship Learning
Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image.
Unifying Nonlocal Blocks for Neural Networks
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks.
Recovering the Unbiased Scene Graphs from the Biased Ones
Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
Ranked #2 on
Unbiased Scene Graph Generation
on Visual Genome
Inter-intra Variant Dual Representations forSelf-supervised Video Recognition
Contrastive learning applied to self-supervised representation learning has seen a resurgence in deep models.
Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning
In particular, we decouple the training of the representation and the classifier, and systematically investigate the effects of different data re-sampling techniques when training the whole network including a classifier as well as fine-tuning the feature extractor only.
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.
Center Prediction Loss for Re-identification
In this paper, we propose a new loss based on center predictivity, that is, a sample must be positioned in a location of the feature space such that from it we can roughly predict the location of the center of same-class samples.
ConTNet: Why not use convolution and transformer at the same time?
It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.
Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
We extract degradation prior at task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet).
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
In this paper, we propose MINE to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image.
TransFG: A Transformer Architecture for Fine-grained Recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
Involution: Inverting the Inherence of Convolution for Visual Recognition
Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision.
Ranked #703 on
Image Classification
on ImageNet
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
In this paper, a retrieval-based coarse-to-fine framework is proposed, where we re-rank the TopN classification results by using the local region enhanced embedding features to improve the Top1 accuracy (based on the observation that the correct category usually resides in TopN results).
Ranked #10 on
Fine-Grained Image Classification
on Stanford Cars
Fine-Grained Image Classification
Fine-Grained Image Recognition
+2
Incorporating Vision Bias into Click Models for Image-oriented Search Engine
Most typical click models assume that the probability of a document to be examined by users only depends on position, such as PBM and UBM.
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective
A popular attempts towards the challenge is unpaired generative adversarial networks, which generate "real" LR counterparts from real HR images using image-to-image translation and then perform super-resolution from "real" LR->SR.
Domain-Invariant Disentangled Network for Generalizable Object Detection
By disentangling representations on both image and instance levels, DIDN is able to learn domain-invariant representations that are suitable for generalized object detection.
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks
When choosing Chebyshev graph filter, a generalized formulation can be derived for explaining the existing nonlocal-based blocks (e. g. nonlocal block, nonlocal stage, double attention block) and uses to analyze their irrationality.
TransTrack: Multiple Object Tracking with Transformer
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.
Ranked #6 on
Multi-Object Tracking
on SportsMOT
(using extra training data)
Multi-Object Tracking
Multiple Object Tracking with Transformer
+3
What Makes for End-to-End Object Detection?
We identify that classification cost in matching cost is the main ingredient: (1) previous detectors only consider location cost, (2) by additionally introducing classification cost, previous detectors immediately produce one-to-one prediction during inference.
Slimmable Generative Adversarial Networks
In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module.
Ranked #6 on
Unsupervised Video Object Segmentation
on FBMS test
Semantic Segmentation
Unsupervised Video Object Segmentation
+1
Is normalization indispensable for training deep neural network?
In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
Learning the Best Pooling Strategy for Visual Semantic Embedding
Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions.
Towards Good Practices for Multi-Person Pose Estimation
Multi-Person Pose Estimation is an interesting yet challenging task in computer vision.
Towards Good Practices for Instance Segmentation
Instance Segmentation is an interesting yet challenging task in computer vision.
Towards Good Practices for Video Object Segmentation
Semi-supervised video object segmentation is an interesting yet challenging task in machine learning.
Deformable Tube Network for Action Detection in Videos
To perform action detection, we design a 3D convolution network with skip connections for tube classification and regression.
A Context-and-Spatial Aware Network for Multi-Person Pose Estimation
In this paper, a novel Context-and-Spatial Aware Network (CSANet), which integrates both a Context Aware Path and Spatial Aware Path, is proposed to obtain effective features involving both context information and spatial information.
Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
Multi-person pose estimation is an important but challenging problem in computer vision.
Generative Dual Adversarial Network for Generalized Zero-shot Learning
Most previous models try to learn a fixed one-directional mapping between visual and semantic space, while some recently proposed generative methods try to generate image features for unseen classes so that the zero-shot learning problem becomes a traditional fully-supervised classification problem.
Mask Propagation Network for Video Object Segmentation
In this work, we propose a mask propagation network to treat the video segmentation problem as a concept of the guided instance segmentation.
Knowing Where to Look? Analysis on Attention of Visual Question Answering System
Attention mechanisms have been widely used in Visual Question Answering (VQA) solutions due to their capacity to model deep cross-domain interactions.
Towards Good Practices for Multi-modal Fusion in Large-scale Video Classification
Leveraging both visual frames and audio has been experimentally proven effective to improve large-scale video classification.
An Introduction to Image Synthesis with Generative Adversarial Nets
There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years.
Network Iterative Learning for Dynamic Deep Neural Networks via Morphism
In this research, we present a novel learning scheme called network iterative learning for deep neural networks.
MAT: A Multimodal Attentive Translator for Image Captioning
In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model.
Modularized Morphing of Neural Networks
Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i. e., how a convolutional layer can be morphed into an arbitrary module of a neural network.
Surveillance Video Parsing with Single Frame Supervision
In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage.
Network Morphism
The second requirement for this network morphism is its ability to deal with non-linearity in a network.
Robust Image Segmentation Using Contour-Guided Color Palettes
The contour-guided color palette (CCP) is proposed for robust image segmentation.
Understanding Image Structure via Hierarchical Shape Parsing
A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.
