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Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution
With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.
RSL19BD at DBDC4: Ensemble of Decision Tree-based and LSTM-based Models
RSL19BD (Waseda University Sakai Laboratory) participated in the Fourth Dialogue Breakdown Detection Challenge (DBDC4) and submitted five runs to both English and Japanese subtasks.
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