research
∙
08/01/2023
PVG: Progressive Vision Graph for Vision Recognition
Convolution-based and Transformer-based vision backbone networks process...
share
research
∙
07/12/2023
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation
Glass-like objects are widespread in daily life but remain intractable t...
research
∙
05/30/2023
Align, Perturb and Decouple: Toward Better Leverage of Difference Information for RSI Change Detection
Change detection is a widely adopted technique in remote sense imagery (...
research
∙
05/19/2023
Learning Global-aware Kernel for Image Harmonization
Image harmonization aims to solve the visual inconsistency problem in co...
research
∙
05/12/2023
Hear to Segment: Unmixing the Audio to Guide the Semantic Segmentation
In this paper, we focus on a recently proposed novel task called Audio-V...
research
∙
04/07/2023
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Most of the existing blind image Super-Resolution (SR) methods assume th...
research
∙
03/01/2023
Multimodal Industrial Anomaly Detection via Hybrid Fusion
2D-based Industrial Anomaly Detection has been widely discussed, however...
research
∙
01/06/2023
Exploring Efficient Few-shot Adaptation for Vision Transformers
The task of Few-shot Learning (FSL) aims to do the inference on novel ca...
research
∙
01/03/2023
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Few Shot Instance Segmentation (FSIS) requires models to detect and segm...
research
∙
11/30/2022
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Positive-Unlabeled (PU) learning aims to learn a model with rare positiv...
research
∙
11/29/2022
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
The task of Few-shot learning (FSL) aims to transfer the knowledge learn...
research
∙
08/23/2022
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Training deep neural network (DNN) with noisy labels is practically chal...
research
∙
07/14/2022
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on ...
research
∙
06/19/2022
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Motivated by biological evolution, this paper explains the rationality o...
research
∙
03/11/2022
Learning Distinctive Margin toward Active Domain Adaptation
Despite plenty of efforts focusing on improving the domain adaptation ab...
research
∙
01/12/2022
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
In the practical application of restoring low-resolution gray-scale imag...
research
∙
12/10/2021
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to learn object locali...
research
∙
10/19/2021
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context
In this paper, we place the atomic action detection problem into a Long-...
research
∙
07/27/2021
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
Localizing individuals in crowds is more in accordance with the practica...
research
∙
07/27/2021
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
Recently, the problem of inaccurate learning targets in crowd counting d...
research
∙
06/04/2021
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
Non-Maximum Suppression (NMS) is essential for object detection and affe...
research
∙
05/31/2021
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision T...
research
∙
05/24/2021
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
Recently, most siamese network based trackers locate targets via object ...
research
∙
03/24/2021
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization
Temporal action localization is an important yet challenging task in vid...
research
∙
03/23/2021
Learning Comprehensive Motion Representation for Action Recognition
For action recognition learning, 2D CNN-based methods are efficient but ...
research
∙
12/31/2020
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Most recent semantic segmentation methods adopt a fully-convolutional ne...
research
∙
07/30/2020
Dense Scene Multiple Object Tracking with Box-Plane Matching
Multiple Object Tracking (MOT) is an important task in computer vision. ...
research
∙
07/29/2020
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Existing Multiple-Object Tracking (MOT) methods either follow the tracki...
research
∙
07/15/2020
Temporal Distinct Representation Learning for Action Recognition
Motivated by the previous success of Two-Dimensional Convolutional Neura...
research
∙
07/02/2020
ACFD: Asymmetric Cartoon Face Detector
Cartoon face detection is a more challenging task than human face detect...
research
∙
03/29/2020
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision f...
research
∙
03/25/2020
ASFD: Automatic and Scalable Face Detector
In this paper, we propose a novel Automatic and Scalable Face Detector (...
research
∙
11/21/2019
TEINet: Towards an Efficient Architecture for Video Recognition
Efficiency is an important issue in designing video architectures for ac...
research
∙
11/11/2019
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
Generating temporal action proposals remains a very challenging problem,...
research
∙
10/24/2018
