96 research outputs found
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
International audienceLearning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a novel multi-task learning algorithm called MT-Adaboost: it extends Adaboost algorithm to the multi-task setting; it uses as multi-task weak classifier a multi-task decision stump. This allows to learn different dependencies between tasks for different regions of the learning space. Thus, we relax the conventional hypothesis that tasks behave similarly in the whole learning space. Moreover, MT-Adaboost can learn multiple tasks without imposing the constraint of sharing the same label set and/or examples between tasks. A theoretical analysis is derived from the analysis of the original Adaboost. Experiments for multiple tasks over large scale textual data sets with social context (Enron and Tobacco) give rise to very promising results
End-to-End (Instance)-Image Goal Navigation through Correspondence as an Emergent Phenomenon
Most recent work in goal oriented visual navigation resorts to large-scale
machine learning in simulated environments. The main challenge lies in learning
compact representations generalizable to unseen environments and in learning
high-capacity perception modules capable of reasoning on high-dimensional
input. The latter is particularly difficult when the goal is not given as a
category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception
module needs to learn a comparison strategy requiring to solve an underlying
visual correspondence problem. This has been shown to be difficult from reward
alone or with standard auxiliary tasks. We address this problem through a
sequence of two pretext tasks, which serve as a prior for what we argue is one
of the main bottleneck in perception, extremely wide-baseline relative pose
estimation and visibility prediction in complex scenes. The first pretext task,
cross-view completion is a proxy for the underlying visual correspondence
problem, while the second task addresses goal detection and finding directly.
We propose a new dual encoder with a large-capacity binocular ViT model and
show that correspondence solutions naturally emerge from the training signals.
Experiments show significant improvements and SOTA performance on the two
benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics
and height differ between observation and goal
Intelligent Self-Repairable Web Wrappers
The amount of information available on the Web grows at an incredible high rate. Systems and procedures devised to extract these data from Web sources already exist, and different approaches and techniques have been investigated during the last years. On the one hand, reliable solutions should provide robust algorithms of Web data mining which could automatically face possible malfunctioning or failures. On the other, in literature there is a lack of solutions about the maintenance of these systems. Procedures that extract Web data may be strictly interconnected with the structure of the data source itself; thus, malfunctioning or acquisition of corrupted data could be caused, for example, by structural modifications of data sources brought by their owners. Nowadays, verification of data integrity and maintenance are mostly manually managed, in order to ensure that these systems work correctly and reliably. In this paper we propose a novel approach to create procedures able to extract data from Web sources -- the so called Web wrappers -- which can face possible malfunctioning caused by modifications of the structure of the data source, and can automatically repair themselves.\u
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks,
self-supervised pre-training methods have not yet fully delivered on dense
geometric vision tasks such as stereo matching or optical flow. The application
of self-supervised concepts, such as instance discrimination or masked image
modeling, to geometric tasks is an active area of research. In this work, we
build on the recent cross-view completion framework, a variation of masked
image modeling that leverages a second view from the same scene which makes it
well suited for binocular downstream tasks. The applicability of this concept
has so far been limited in at least two ways: (a) by the difficulty of
collecting real-world image pairs -- in practice only synthetic data have been
used -- and (b) by the lack of generalization of vanilla transformers to dense
downstream tasks for which relative position is more meaningful than absolute
position. We explore three avenues of improvement. First, we introduce a method
to collect suitable real-world image pairs at large scale. Second, we
experiment with relative positional embeddings and show that they enable vision
transformers to perform substantially better. Third, we scale up vision
transformer based cross-completion architectures, which is made possible by the
use of large amounts of data. With these improvements, we show for the first
time that state-of-the-art results on stereo matching and optical flow can be
reached without using any classical task-specific techniques like correlation
volume, iterative estimation, image warping or multi-scale reasoning, thus
paving the way towards universal vision models.Comment: ICCV 202
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