286 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
Putting the Object Back into Video Object Segmentation
We present Cutie, a video object segmentation (VOS) network with object-level
memory reading, which puts the object representation from memory back into the
video object segmentation result. Recent works on VOS employ bottom-up
pixel-level memory reading which struggles due to matching noise, especially in
the presence of distractors, resulting in lower performance in more challenging
data. In contrast, Cutie performs top-down object-level memory reading by
adapting a small set of object queries for restructuring and interacting with
the bottom-up pixel features iteratively with a query-based object transformer
(qt, hence Cutie). The object queries act as a high-level summary of the target
object, while high-resolution feature maps are retained for accurate
segmentation. Together with foreground-background masked attention, Cutie
cleanly separates the semantics of the foreground object from the background.
On the challenging MOSE dataset, Cutie improves by 8.7 J&F over XMem with a
similar running time and improves by 4.2 J&F over DeAOT while running three
times as fast. Code is available at: https://hkchengrex.github.io/CutieComment: Project page: https://hkchengrex.github.io/Cuti
One Shot Learning with class partitioning and cross validation voting (CP-CVV)
Producción CientíficaOne Shot Learning includes all those techniques that make it possible to classify images using a single image per category. One of its possible applications is the identification of food products. For a grocery store, it is interesting to record a single image of each product and be able to recognise it again from other images, such as photos taken by customers. Within deep learning, Siamese neural networks are able to verify whether two images belong to the same category or not. In this paper, a new Siamese network training technique, called CP-CVV, is presented. It uses the combination of different models trained with different classes. The separation of validation classes has been done in such a way that each of the combined models is different in order to avoid overfitting with respect to the validation. Unlike normal training, the test images belong to classes that have not previously been used in training, allowing the model to work on new categories, of which only one image exists. Different backbones have been evaluated in the Siamese composition, but also the integration of multiple models with different backbones. The results show that the model improves on previous works and allows the classification problem to be solved, an additional step towards the use of Siamese networks. To the best of our knowledge, there is no existing work that has proposed integrating Siamese neural networks using a class-based validation set separation technique so as to be better at generalising for unknown classes. Additionally, we have applied Cross-Validation-Voting with ConvNeXt to improve the existing classification results of a well-known Grocery Store Dataset.The Centre for the Development of Industrial Technology (CDTI) and by the Instituto para la Competitividad Empresarial de Castilla y León - FEDER (Project CCTT3/20/VA/0003
The Computational Power of Optimization in Online Learning
We consider the fundamental problem of prediction with expert advice where
the experts are "optimizable": there is a black-box optimization oracle that
can be used to compute, in constant time, the leading expert in retrospect at
any point in time. In this setting, we give a novel online algorithm that
attains vanishing regret with respect to experts in total
computation time. We also give a lower bound showing
that this running time cannot be improved (up to log factors) in the oracle
model, thereby exhibiting a quadratic speedup as compared to the standard,
oracle-free setting where the required time for vanishing regret is
. These results demonstrate an exponential gap between
the power of optimization in online learning and its power in statistical
learning: in the latter, an optimization oracle---i.e., an efficient empirical
risk minimizer---allows to learn a finite hypothesis class of size in time
. We also study the implications of our results to learning in
repeated zero-sum games, in a setting where the players have access to oracles
that compute, in constant time, their best-response to any mixed strategy of
their opponent. We show that the runtime required for approximating the minimax
value of the game in this setting is , yielding
again a quadratic improvement upon the oracle-free setting, where
is known to be tight
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