Papers Feature Upsampling
“Feature Upsampling” 태그가 달린 논문 39편 · 필터 해제
FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail…
DecoderFeature UpsamplingImage MattingSemantic SegmentationA Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
Feature upsampling is a fundamental and indispensable ingredient of almost all current network structures for image segmentation tasks. Recently, a popular similarity-based feature upsampling pipeline has been proposed, …
Feature UpsamplingImage SegmentationSemantic SegmentationLiFT: A Surprisingly Simple Lightweight Feature Transform for Dense ViT Descriptors
We present a simple self-supervised method to enhance the performance of ViT features for dense downstream tasks. Our Lightweight Feature Transform (LiFT) is a straightforward and compact postprocessing network that can …
Feature UpsamplingObject DiscoveryFeatUp: A Model-Agnostic Framework for Features at Any Resolution
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime. However, these features often lack the spa…
Depth EstimationDepth PredictionFeature UpsamplingImage Super-Resolution+5ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps an…
Feature UpsamplingImage ReconstructionImproving Depth Completion via Depth Feature Upsampling
The encoder-decoder network (ED-Net) is a commonly employed choice for existing depth completion methods but its working mechanism is ambiguous. In this paper we visualize the internal feature maps to analyze how the…
DecoderDepth CompletionFeature UpsamplingLearning to Upsample by Learning to Sample
We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce…
Depth EstimationFeature UpsamplingGPUInstance Segmentation+6On Point Affiliation in Feature Upsampling
We introduce the notion of point affiliation into feature upsampling. By abstracting a feature map into non-overlapped semantic clusters formed by points of identical semantic meaning, feature upsampling can be viewed as…
DecoderDepth EstimationFeature UpsamplingImage Matting+6SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized. Howeve…
Feature Upsamplingimage-classificationImage Classificationobject-detection+3SAPA: Similarity-Aware Point Affiliation for Feature Upsampling
We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking…
DecoderDepth EstimationFeature UpsamplingImage Matting+5FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks suc…
DecoderFeature UpsamplingImage MattingSemantic SegmentationLocal and Global GANs with Semantic-Aware Upsampling for Image Generation
In this paper, we address the task of semantic-guided image generation. One challenge common to most existing image-level generation methods is the difficulty in generating small objects and detailed local textures. To a…
Feature UpsamplingImage GenerationDeep ViT Features as Dense Visual Descriptors
We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as dense visual descriptors. We observe and empirically demonstrate that such features, when extractedfrom a self-supervised ViT mode…
Feature UpsamplingSemantic correspondenceJoint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images
Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has …
DemosaickingDenoisingFeature UpsamplingCARAFE: Content-Aware ReAssembly of FEatures
Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance seg…
Feature UpsamplingInstance Segmentationobject-detectionObject Detection+1Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (C…
Feature UpsamplingLesion SegmentationSegmentationSemantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
Semantic image segmentation is one of the most challenged tasks in computer vision. In this paper, we propose a highly fused convolutional network, which consists of three parts: feature downsampling, combined feature up…
Feature UpsamplingImage SegmentationSemantic SegmentationDeep Image Prior
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of examp…
DenoisingFeature UpsamplingImage DenoisingImage Generation+5A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. In the proposal sub-netwo…
Face DetectionFeature UpsamplingObjectobject-detection+3