311 research outputs found
Totally Corrective Multiclass Boosting with Binary Weak Learners
In this work, we propose a new optimization framework for multiclass boosting
learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two
successful multiclass boosting algorithms, which can use binary weak learners.
We explicitly derive these two algorithms' Lagrange dual problems based on
their regularized loss functions. We show that the Lagrange dual formulations
enable us to design totally-corrective multiclass algorithms by using the
primal-dual optimization technique. Experiments on benchmark data sets suggest
that our multiclass boosting can achieve a comparable generalization capability
with state-of-the-art, but the convergence speed is much faster than stage-wise
gradient descent boosting. In other words, the new totally corrective
algorithms can maximize the margin more aggressively.Comment: 11 page
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
catena-Poly[[[triaquaÂcopper(II)]-μ-pyridine-2,3-dicarboxylÂato-κ3 N,O 2:O 3] monohydrate]
In the title compound, {[Cu(C7H3NO4)(H2O)3]·H2O}n, the CuII ion is bonded to three water molÂecules, one N,O-bidentate pyridine-2,3-dicarboxylÂate dianion and one O-bonded symmetry-generated dianion, resulting in a distorted CuNO5 octaÂhedral geometry. The bridging ligand results in an infinite chain. A network of O—H⋯O hydrogen bonds helps to establish the crystal structure
RGM: A Robust Generalist Matching Model
Finding corresponding pixels within a pair of images is a fundamental
computer vision task with various applications. Due to the specific
requirements of different tasks like optical flow estimation and local feature
matching, previous works are primarily categorized into dense matching and
sparse feature matching focusing on specialized architectures along with
task-specific datasets, which may somewhat hinder the generalization
performance of specialized models. In this paper, we propose a deep model for
sparse and dense matching, termed RGM (Robust Generalist Matching). In
particular, we elaborately design a cascaded GRU module for refinement by
exploring the geometric similarity iteratively at multiple scales following an
additional uncertainty estimation module for sparsification. To narrow the gap
between synthetic training samples and real-world scenarios, we build a new,
large-scale dataset with sparse correspondence ground truth by generating
optical flow supervision with greater intervals. As such, we are able to mix up
various dense and sparse matching datasets, significantly improving the
training diversity. The generalization capacity of our proposed RGM is greatly
improved by learning the matching and uncertainty estimation in a two-stage
manner on the large, mixed data. Superior performance is achieved for zero-shot
matching and downstream geometry estimation across multiple datasets,
outperforming the previous methods by a large margin.Comment: 17 pages. Fixed typo in the first two equations. Code is available
at: https://github.com/aim-uofa/RG
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