197 research outputs found
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
SAPA: 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 point
affiliation, we present a generic formulation for generating upsampling
kernels. The kernels encourage not only semantic smoothness but also boundary
sharpness in the upsampled feature maps. Such properties are particularly
useful for some dense prediction tasks such as semantic segmentation. The key
idea of our formulation is to generate similarity-aware kernels by comparing
the similarity between each encoder feature point and the spatially associated
local region of decoder features. In this way, the encoder feature point can
function as a cue to inform the semantic cluster of upsampled feature points.
To embody the formulation, we further instantiate a lightweight upsampling
operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its
variants. SAPA invites consistent performance improvements on a number of dense
prediction tasks, including semantic segmentation, object detection, depth
estimation, and image matting. Code is available at:
https://github.com/poppinace/sapaComment: Accepted to NeurIPS 2022. Code is available at
https://github.com/poppinace/sap
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