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Found 30 papers, 16 papers with code
Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction
In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer with shared gating for multi-task learning of dense prediction.
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
Beyond the normal case, long-tail class incremental learning and few-shot class incremental learning are also proposed to consider the data imbalance and data scarcity, respectively, which are common in real-world implementations and further exacerbate the well-known problem of catastrophic forgetting.
class-incremental learning
Few-Shot Class-Incremental Learning
+1
Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers
SGD and Adam are two classical and effective optimizers on which researchers have proposed many variants, such as SGDM and RAdam.
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation
Moreover, a bidirectional vision-language interaction module is implemented before the multimodal Transformer to enhance the correlation between the visual and linguistic features, thus facilitating the language queries to decode more precise object information from visual features and ultimately improving the segmentation performance.
Referring Video Object Segmentation
Representation Learning
+2
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.
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction
To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework.
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration
This is especially inappropriate for data-starved hyperspectral image (HSI) restoration.
Centroid-centered Modeling for Efficient Vision Transformer Pre-training
Our proposed approach, \textbf{CCViT}, leverages k-means clustering to obtain centroids for image modeling without supervised training of tokenizer model.
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer for multi-task learning of dense prediction.
Learning to Learn Better for Video Object Segmentation
Recently, the joint learning framework (JOINT) integrates matching based transductive reasoning and online inductive learning to achieve accurate and robust semi-supervised video object segmentation (SVOS).
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer
In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration.
Multi-Task Learning with Multi-Query Transformer for Dense Prediction
We first model each task with a task-relevant query.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Siamese Network with Interactive Transformer for Video Object Segmentation
Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously.
Monocular Road Planar Parallax Estimation
It implies planar parallax and can be combined with the road plane serving as a reference to estimate the 3D structure by warping the consecutive frames.
Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
In this paper, we propose a novel stagewise domain adaptation model called RoadDA to address the DS issue in this field.
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
In addition, feature-level alignment is carried out by aligning the feature maps of the source and target images from student network using a weighted maximum mean discrepancy loss.
Ranked #17 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery
Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.
LocalDrop: A Hybrid Regularization for Deep Neural Networks
In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas.
Recurrent Feature Reasoning for Image Inpainting
To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.
Exploring Representativeness and Informativeness for Active Learning
Then can we find a way to fuse the two active sampling criteria without any assumption on data?
Robust and Discriminative Labeling for Multi-label Active Learning Based on Maximum Correntropy Criterion
Meanwhile, it is also hard to build a good model without diagnosing discriminative labels.
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
Defect Detection from UAV Images based on Region-Based CNNs
In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.
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 #15 on
Unsupervised Domain Adaptation
on Market to Duke
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy.
