141,976 research outputs found
Person Re-identification with Deep Similarity-Guided Graph Neural Network
The person re-identification task requires to robustly estimate visual
similarities between person images. However, existing person re-identification
models mostly estimate the similarities of different image pairs of probe and
gallery images independently while ignores the relationship information between
different probe-gallery pairs. As a result, the similarity estimation of some
hard samples might not be accurate. In this paper, we propose a novel deep
learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to
overcome such limitations. Given a probe image and several gallery images,
SGGNN creates a graph to represent the pairwise relationships between
probe-gallery pairs (nodes) and utilizes such relationships to update the
probe-gallery relation features in an end-to-end manner. Accurate similarity
estimation can be achieved by using such updated probe-gallery relation
features for prediction. The input features for nodes on the graph are the
relation features of different probe-gallery image pairs. The probe-gallery
relation feature updating is then performed by the messages passing in SGGNN,
which takes other nodes' information into account for similarity estimation.
Different from conventional GNN approaches, SGGNN learns the edge weights with
rich labels of gallery instance pairs directly, which provides relation fusion
more precise information. The effectiveness of our proposed method is validated
on three public person re-identification datasets.Comment: accepted to ECCV 201
Rank Supervised Contrastive Learning for Time Series Classification
Recently, various contrastive learning techniques have been developed to
categorize time series data and exhibit promising performance. A general
paradigm is to utilize appropriate augmentations and construct feasible
positive samples such that the encoder can yield robust and discriminative
representations by mapping similar data points closer together in the feature
space while pushing dissimilar data points farther apart. Despite its efficacy,
the fine-grained relative similarity (e.g., rank) information of positive
samples is largely ignored, especially when labeled samples are limited. To
this end, we present Rank Supervised Contrastive Learning (RankSCL) to perform
time series classification. Different from conventional contrastive learning
frameworks, RankSCL augments raw data in a targeted way in the embedding space
and adopts certain filtering rules to select more informative positive and
negative pairs of samples. Moreover, a novel rank loss is developed to assign
different weights for different levels of positive samples, enable the encoder
to extract the fine-grained information of the same class, and produce a clear
boundary among different classes. Thoroughly empirical studies on 128 UCR
datasets and 30 UEA datasets demonstrate that the proposed RankSCL can achieve
state-of-the-art performance compared to existing baseline methods
Adaptive image retrieval using a graph model for semantic feature integration
The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the
retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe
how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)
Deriving item features relevance from collaborative domain knowledge
An Item based recommender system works by computing a similarity between
items, which can exploit past user interactions (collaborative filtering) or
item features (content based filtering). Collaborative algorithms have been
proven to achieve better recommendation quality then content based algorithms
in a variety of scenarios, being more effective in modeling user behaviour.
However, they can not be applied when items have no interactions at all, i.e.
cold start items. Content based algorithms, which are applicable to cold start
items, often require a lot of feature engineering in order to generate useful
recommendations. This issue is specifically relevant as the content descriptors
become large and heterogeneous. The focus of this paper is on how to use a
collaborative models domain-specific knowledge to build a wrapper feature
weighting method which embeds collaborative knowledge in a content based
algorithm. We present a comparative study for different state of the art
algorithms and present a more general model. This machine learning approach to
feature weighting shows promising results and high flexibility
Learning to select data for transfer learning with Bayesian Optimization
Domain similarity measures can be used to gauge adaptability and select
suitable data for transfer learning, but existing approaches define ad hoc
measures that are deemed suitable for respective tasks. Inspired by work on
curriculum learning, we propose to \emph{learn} data selection measures using
Bayesian Optimization and evaluate them across models, domains and tasks. Our
learned measures outperform existing domain similarity measures significantly
on three tasks: sentiment analysis, part-of-speech tagging, and parsing. We
show the importance of complementing similarity with diversity, and that
learned measures are -- to some degree -- transferable across models, domains,
and even tasks.Comment: EMNLP 2017. Code available at:
https://github.com/sebastianruder/learn-to-select-dat
Towards Real-World Visual Tracking with Temporal Contexts
Visual tracking has made significant improvements in the past few decades.
Most existing state-of-the-art trackers 1) merely aim for performance in ideal
conditions while overlooking the real-world conditions; 2) adopt the
tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only
integrate the temporal information into the template, where temporal contexts
among consecutive frames are far from being fully utilized. To handle those
problems, we propose a two-level framework (TCTrack) that can exploit temporal
contexts efficiently. Based on it, we propose a stronger version for real-world
visual tracking, i.e., TCTrack++. It boils down to two levels: features and
similarity maps. Specifically, for feature extraction, we propose an
attention-based temporally adaptive convolution to enhance the spatial features
using temporal information, which is achieved by dynamically calibrating the
convolution weights. For similarity map refinement, we introduce an adaptive
temporal transformer to encode the temporal knowledge efficiently and decode it
for the accurate refinement of the similarity map. To further improve the
performance, we additionally introduce a curriculum learning strategy. Also, we
adopt online evaluation to measure performance in real-world conditions.
Exhaustive experiments on 8 wellknown benchmarks demonstrate the superiority of
TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used
in real-world applications.Comment: Accepted by IEEE TPAMI, Code:
https://github.com/vision4robotics/TCTrac
Memory-Based Learning: Using Similarity for Smoothing
This paper analyses the relation between the use of similarity in
Memory-Based Learning and the notion of backed-off smoothing in statistical
language modeling. We show that the two approaches are closely related, and we
argue that feature weighting methods in the Memory-Based paradigm can offer the
advantage of automatically specifying a suitable domain-specific hierarchy
between most specific and most general conditioning information without the
need for a large number of parameters. We report two applications of this
approach: PP-attachment and POS-tagging. Our method achieves state-of-the-art
performance in both domains, and allows the easy integration of diverse
information sources, such as rich lexical representations.Comment: 8 pages, uses aclap.sty, To appear in Proc. ACL/EACL 9
A Systematic Comparison of Music Similarity Adaptation Approaches
In order to support individual user perspectives and different retrieval tasks, music similarity can no longer be considered as a static element of Music Information Retrieval (MIR) systems. Various approaches have been proposed recently that allow dynamic adaptation of music similarity measures. This paper provides a systematic comparison of algorithms for metric learning and higher-level facet distance weighting on the MagnaTagATune dataset. A crossvalidation variant taking into account clip availability is presented. Applied on user generated similarity data, its effect on adaptation performance is analyzed. Special attention is paid to the amount of training data necessary for making similarity predictions on unknown data, the number of model parameters and the amount of information available about the music itself. 1
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