2,082 research outputs found
What-if analysis: A visual analytics approach to Information Retrieval evaluation
This paper focuses on the innovative visual analytics approach realized by the Visual Analytics Tool for Experimental Evaluation (VATE2) system, which eases and makes more effective the experimental evaluation process by introducing the what-if analysis. The what-if analysis is aimed at estimating the possible effects of a modification to an Information Retrieval (IR) system, in order to select the most promising fixes before implementing them, thus saving a considerable amount of effort. VATE2 builds on an analytical framework which models the behavior of the systems in order to make estimations, and integrates this analytical framework into a visual part which, via proper interaction and animations, receives input and provides feedback to the user. We conducted an experimental evaluation to assess the numerical performances of the analytical model and a validation of the visual analytics prototype with domain experts. Both the numerical evaluation and the user validation have shown that VATE2 is effective, innovative, and useful
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
The code is available online at:
https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory
The comparison of benchmark error sets is an essential tool for the
evaluation of theories in computational chemistry. The standard ranking of
methods by their Mean Unsigned Error is unsatisfactory for several reasons
linked to the non-normality of the error distributions and the presence of
underlying trends. Complementary statistics have recently been proposed to
palliate such deficiencies, such as quantiles of the absolute errors
distribution or the mean prediction uncertainty. We introduce here a new score,
the systematic improvement probability (SIP), based on the direct system-wise
comparison of absolute errors. Independently of the chosen scoring rule, the
uncertainty of the statistics due to the incompleteness of the benchmark data
sets is also generally overlooked. However, this uncertainty is essential to
appreciate the robustness of rankings. In the present article, we develop two
indicators based on robust statistics to address this problem: P_{inv}, the
inversion probability between two values of a statistic, and \mathbf{P}_{r},
the ranking probability matrix. We demonstrate also the essential contribution
of the correlations between error sets in these scores comparisons
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
IRIM at TRECVID 2011: Semantic Indexing and Instance Search
12 pages - TRECVID workshop notebook papers/slides available at http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.htmlInternational audienceThe IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2011 se- mantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likeli- hood of a video shot to contain a target concept. These scores are then used for producing a ranked list of im- ages or shots that are the most likely to contain the tar- get concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.1387, which ranked us 5th out of 19 partic- ipants. For the instance search task, we we used both object based query and frame based query. We formu- lated the query in standard way as comparison of visual signatures either of object with parts of DB frames or as a comparison of visual signatures of query and DB frames. To produce visual signatures we also used two apporaches: the first one is the baseline Bag-Of-Visual- Words (BOVW) model based on SURF interest point descriptor; the second approach is a Bag-Of-Regions (BOR) model that extends the traditional notion of BOVW vocabulary not only to keypoint-based descrip- tors but to region based descriptors
The Global Trends of Total Factor Productivity. Evidence from the Nonparametric Malmquist Index Approach
In this paper the Malmquist index of total factor productivity is applied to a sample of 87 countries observed over the period 1960-90. This index and the method needed to quantify it, the data envelopment analysis, has substantial advantages as compared to traditional growth accounting. Two of these advantages are that it does not rely on questionable equilibrium assumptions to merge multiple inputs into a single index and that the rate of total factor productivity growth can explicitly be decomposed into a measure of efficiency change and the rate of technological progress. Results are reported both in the form of growth rates and measures of relative productivity levels. In each case related labour productivity measures are calculated and the differences to the total factor productivity measures are analysed. Among the topics covered are the productivity slowdown, the Asian Miracle and the bimodality of the distribution of relative producitivity levels.nonparametric productivity measurement, Malmquist index, growth accounting, productivity levels, distribution dynamics
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