64 research outputs found
Planar Object Tracking in the Wild: A Benchmark
Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201
Studies on the Effect of Applying Fertilizer on \u3cem\u3ePinus tabulaeformi\u3c/em\u3e in Shen Dong Mine
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
Query intent classification, which aims at assisting customers to find
desired products, has become an essential component of the e-commerce search.
Existing query intent classification models either design more exquisite models
to enhance the representation learning of queries or explore label-graph and
multi-task to facilitate models to learn external information. However, these
models cannot capture multi-granularity matching features from queries and
categories, which makes them hard to mitigate the gap in the expression between
informal queries and categories.
This paper proposes a Multi-granularity Matching Attention Network (MMAN),
which contains three modules: a self-matching module, a char-level matching
module, and a semantic-level matching module to comprehensively extract
features from the query and a query-category interaction matrix. In this way,
the model can eliminate the difference in expression between queries and
categories for query intent classification. We conduct extensive offline and
online A/B experiments, and the results show that the MMAN significantly
outperforms the strong baselines, which shows the superiority and effectiveness
of MMAN. MMAN has been deployed in production and brings great commercial value
for our company.Comment: Accepted by WWW 202
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