36,559 research outputs found
Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed
skepticism that neural ranking models were actually improving ad hoc retrieval
effectiveness in limited data scenarios. He provided anecdotal evidence that
authors of neural IR papers demonstrate "wins" by comparing against weak
baselines. This paper provides a rigorous evaluation of those claims in two
ways: First, we conducted a meta-analysis of papers that have reported
experimental results on the TREC Robust04 test collection. We do not find
evidence of an upward trend in effectiveness over time. In fact, the best
reported results are from a decade ago and no recent neural approach comes
close. Second, we applied five recent neural models to rerank the strong
baselines that Lin used to make his arguments. A significant improvement was
observed for one of the models, demonstrating additivity in gains. While there
appears to be merit to neural IR approaches, at least some of the gains
reported in the literature appear illusory.Comment: Published in the Proceedings of the 42nd Annual International ACM
SIGIR Conference on Research and Development in Information Retrieval (SIGIR
2019
Clothing Co-Parsing by Joint Image Segmentation and Labeling
This paper aims at developing an integrated system of clothing co-parsing, in
order to jointly parse a set of clothing images (unsegmented but annotated with
tags) into semantic configurations. We propose a data-driven framework
consisting of two phases of inference. The first phase, referred as "image
co-segmentation", iterates to extract consistent regions on images and jointly
refines the regions over all images by employing the exemplar-SVM (E-SVM)
technique [23]. In the second phase (i.e. "region co-labeling"), we construct a
multi-image graphical model by taking the segmented regions as vertices, and
incorporate several contexts of clothing configuration (e.g., item location and
mutual interactions). The joint label assignment can be solved using the
efficient Graph Cuts algorithm. In addition to evaluate our framework on the
Fashionista dataset [30], we construct a dataset called CCP consisting of 2098
high-resolution street fashion photos to demonstrate the performance of our
system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89%
recognition rate on the Fashionista and the CCP datasets, respectively, which
are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201
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