17 research outputs found
Look, Listen and Learn
We consider the question: what can be learnt by looking at and listening to a
large number of unlabelled videos? There is a valuable, but so far untapped,
source of information contained in the video itself -- the correspondence
between the visual and the audio streams, and we introduce a novel
"Audio-Visual Correspondence" learning task that makes use of this. Training
visual and audio networks from scratch, without any additional supervision
other than the raw unconstrained videos themselves, is shown to successfully
solve this task, and, more interestingly, result in good visual and audio
representations. These features set the new state-of-the-art on two sound
classification benchmarks, and perform on par with the state-of-the-art
self-supervised approaches on ImageNet classification. We also demonstrate that
the network is able to localize objects in both modalities, as well as perform
fine-grained recognition tasks.Comment: Appears in: IEEE International Conference on Computer Vision (ICCV)
201
End-to-end weakly-supervised semantic alignment
We tackle the task of semantic alignment where the goal is to compute dense
semantic correspondence aligning two images depicting objects of the same
category. This is a challenging task due to large intra-class variation,
changes in viewpoint and background clutter. We present the following three
principal contributions. First, we develop a convolutional neural network
architecture for semantic alignment that is trainable in an end-to-end manner
from weak image-level supervision in the form of matching image pairs. The
outcome is that parameters are learnt from rich appearance variation present in
different but semantically related images without the need for tedious manual
annotation of correspondences at training time. Second, the main component of
this architecture is a differentiable soft inlier scoring module, inspired by
the RANSAC inlier scoring procedure, that computes the quality of the alignment
based on only geometrically consistent correspondences thereby reducing the
effect of background clutter. Third, we demonstrate that the proposed approach
achieves state-of-the-art performance on multiple standard benchmarks for
semantic alignment.Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2018
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR 2017
Pairwise Quantization
We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
Controllable Attention for Structured Layered Video Decomposition
The objective of this paper is to be able to separate a video into its
natural layers, and to control which of the separated layers to attend to. For
example, to be able to separate reflections, transparency or object motion. We
make the following three contributions: (i) we introduce a new structured
neural network architecture that explicitly incorporates layers (as spatial
masks) into its design. This improves separation performance over previous
general purpose networks for this task; (ii) we demonstrate that we can augment
the architecture to leverage external cues such as audio for controllability
and to help disambiguation; and (iii) we experimentally demonstrate the
effectiveness of our approach and training procedure with controlled
experiments while also showing that the proposed model can be successfully
applied to real-word applications such as reflection removal and action
recognition in cluttered scenes.Comment: In ICCV 201
24/7 place recognition by view synthesis
International audienceWe address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact in-dexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly out-performs other large-scale place recognition techniques on this challenging data
One-Shot Fine-Grained Instance Retrieval
Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale fine-grained identification without a complete training
set. Aiming to conquer this issue, we propose a retrieval task named One-Shot
Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of
identifying unseen objects through a fine-grained retrieval task assisted with
an incomplete auxiliary training set. This paper first presents the detailed
description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we
propose the Convolutional and Normalization Networks (CN-Nets) learned on the
auxiliary dataset to generate a concise and discriminative representation.
Finally, we present a coarse-to-fine retrieval framework consisting of three
components, i.e., coarse retrieval, fine-grained retrieval, and query
expansion, respectively. The framework progressively retrieves images with
similar semantics, and performs fine-grained identification. Experiments show
our OSFGIR framework achieves significantly better accuracy and efficiency than
existing FGVC and image retrieval methods, thus could be a better solution for
large-scale fine-grained object identification.Comment: Accepted by MM2017, 9 pages, 7 figure