7 research outputs found
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention
Scene text recognition (STR) is the task of recognizing character sequences
in natural scenes. While there have been great advances in STR methods, current
methods still fail to recognize texts in arbitrary shapes, such as heavily
curved or rotated texts, which are abundant in daily life (e.g. restaurant
signs, product labels, company logos, etc). This paper introduces a novel
architecture to recognizing texts of arbitrary shapes, named Self-Attention
Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN
utilizes the self-attention mechanism to describe two-dimensional (2D) spatial
dependencies of characters in a scene text image. Exploiting the full-graph
propagation of self-attention, SATRN can recognize texts with arbitrary
arrangements and large inter-character spacing. As a result, SATRN outperforms
existing STR models by a large margin of 5.7 pp on average in "irregular text"
benchmarks. We provide empirical analyses that illustrate the inner mechanisms
and the extent to which the model is applicable (e.g. rotated and multi-line
text). We will open-source the code
The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data
We thought data to be simply given, but reality tells otherwise; it is
costly, situation-dependent, and muddled with dilemmas, constantly requiring
human intervention. The ML community's focus on quality data is increasing in
the same vein, as good data is vital for successful ML systems. Nonetheless,
few works have investigated the dataset builders and the specifics of what they
do and struggle to make good data. In this study, through semi-structured
interviews with 19 ML experts, we present what humans actually do and consider
in each step of the data construction pipeline. We further organize their
struggles under three themes: 1) trade-offs from real-world constraints; 2)
harmonizing assorted data workers for consistency; 3) the necessity of human
intuition and tacit knowledge for processing data. Finally, we discuss why such
struggles are inevitable for good data and what practitioners aspire, toward
providing systematic support for data works