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Analyzing Local Representations of Self-supervised Vision Transformers
v1v2 (latest)

Analyzing Local Representations of Self-supervised Vision Transformers

31 December 2023
Ani Vanyan
Alvard Barseghyan
Hakob Tamazyan
Vahan Huroyan
Hrant Khachatrian
Martin Danelljan
ArXiv (abs)PDFHTML

Papers citing "Analyzing Local Representations of Self-supervised Vision Transformers"

4 / 4 papers shown
Title
SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Kai-Hendrik Cohrs
Zuzanna Osika
Maria Gonzalez-Calabuig
Vishal Nedungadi
Ruben Cartuyvels
Steffen Knoblauch
Joppe Massant
Shruti Nath
Patrick Ebel
Vasileios Sitokonstantinou
68
0
0
13 Nov 2025
Do Satellite Tasks Need Special Pretraining?
Do Satellite Tasks Need Special Pretraining?
Ani Vanyan
Alvard Barseghyan
Hakob Tamazyan
T. Galstyan
Vahan Huroyan
N. Hovakimyan
Hrant Khachatrian
SSLVLM
153
0
0
19 Oct 2025
Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks
Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks
Yannis Kaltampanidis
Alexandros Doumanoglou
D. Zarpalas
112
0
0
18 Sep 2025
DODA: Adapting Object Detectors to Dynamic Agricultural Environments in Real-Time with Diffusion
DODA: Adapting Object Detectors to Dynamic Agricultural Environments in Real-Time with Diffusion
Shuai Xiang
Pieter M. Blok
James Burridge
Haozhou Wang
Wei Guo
348
0
0
27 Mar 2024
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