Papers Feature Upsampling
“Feature Upsampling” 태그가 달린 논문 39편 · 필터 해제
RaysUp: Ultra-light Universal Feature Upsampling via Geometry-Aware Ray Representation
Pre-trained Vision Foundation Models (VFMs) have become central to modern computer vision due to their powerful semantic representations and strong generalization ability. However, their patchified or pooled outputs are …
Feature UpsamplingViT-Up: Faithful Feature Upsampling for Vision Transformers
Vision Transformers (ViTs) have become a dominant architecture for visual representation learning, providing exceptionally strong and broadly reusable backbone features. However, ViTs are commonly operated on relatively …
Semantic correspondenceRepresentation LearningSemantic SegmentationFeature UpsamplingWeighted Reverse Convolution for Feature Upsampling
Pre-trained vision foundation models (VFMs) provide strong semantic representations, yet their patch-level features are inherently coarse, limiting their effectiveness on tasks requiring fine-grained localization, dense …
Video Object SegmentationComputational EfficiencyFeature UpsamplingDepth EstimationDINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery
The remote sensing (RS) domain suffers from a lack of densely labeled datasets, which are costly to obtain. Thus, models that can segment RS imagery well without supervised fine-tuning are valuable, but existing solution…
Open Vocabulary Semantic SegmentationFeature UpsamplingFrozen Vision Transformers for Dense Prediction on Small Datasets: A Case Study in Arrow Localization
We present a system for automated detection, localization, and scoring of arrow punctures on 40\,cm indoor archery target faces, trained on only 48 annotated photographs (5{,}084 punctures). Our pipeline combines three c…
Feature UpsamplingHD-VGGT: High-Resolution Visual Geometry Transformer
High-resolution imagery is essential for accurate 3D reconstruction, as many geometric details only emerge at fine spatial scales. Recent feed-forward approaches, such as the Visual Geometry Grounded Transformer (VGGT), …
Feature Upsampling3D ReconstructionDiveUp: Learning Feature Upsampling from Diverse Vision Foundation Models
Recently, feature upsampling has gained increasing attention owing to its effectiveness in enhancing vision foundation models (VFMs) for pixel-level understanding tasks. Existing methods typically rely on high-resolution…
Feature UpsamplingUPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders
The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act as a shortcut to achieve dense features…
Feature UpsamplingCross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation
Semantic segmentation is a fundamental problem in computer vision and it requires high-resolution feature maps for dense prediction. Current coordinate-guided low-resolution feature interpolation methods, e.g., bilinear …
Semantic SegmentationFeature UpsamplingNAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering
Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off: classical filters are fast and broadly …
Feature UpsamplingImage RestorationUpsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling
We present \textbf{Upsample Anything}, a lightweight test-time optimization (TTO) framework that restores low-resolution features to high-resolution, pixel-wise outputs without any training. Although Vision Foundation Mo…
Semantic SegmentationFeature UpsamplingDepth EstimationAnyUp: Universal Feature Upsampling
We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsamplers for features like DINO or CLIP need t…
Feature UpsamplingMaybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these…
Interactive SegmentationComputational EfficiencyFeature UpsamplingObject DetectionTowards Open-World Human Action Segmentation Using Graph Convolutional Networks
Human-object interaction segmentation is a fundamental task of daily activity understanding, which plays a crucial role in applications such as assistive robotics, healthcare, and autonomous systems. Most existing learni…
Out-of-Distribution DetectionAction SegmentationFeature UpsamplingJAFAR: Jack up Any Feature at Any Resolution
Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities requ…
Feature UpsamplingBenchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentation
Vision Foundation Models (VFMs) are large-scale, pre-trained models that serve as general-purpose backbones for various computer vision tasks. As VFMs' popularity grows, there is an increasing interest in understanding t…
BenchmarkingFeature UpsamplingInteractive SegmentationScene UnderstandingLoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models
Vision foundation models (VFMs) such as DINOv2 and CLIP have achieved impressive results on various downstream tasks, but their limited feature resolution hampers performance in applications requiring pixel-level underst…
Feature UpsamplingLDA-AQU: Adaptive Query-guided Upsampling via Local Deformable Attention
Feature upsampling is an essential operation in constructing deep convolutional neural networks. However, existing upsamplers either lack specific feature guidance or necessitate the utilization of high-resolution featur…
Feature UpsamplingInstance Segmentationobject-detectionObject Detection+2Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels
As a fundamental operation in modern machine vision models, feature upsampling has been widely used and investigated in the literatures. An ideal upsampling operation should be lightweight, with low computational complex…
Feature Upsamplingobject-detectionObject DetectionEfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged
With the widespread application of remote sensing technology in environmental monitoring, the demand for efficient and accurate remote sensing image change detection (CD) for natural environments is growing. We propose a…
Change DetectionFeature Upsampling