7 accepted papers
Generating executable logical forms (LF) using Large Language Models (LLMs) in a few-shot setting for Knowledge Graph Question Answering (KGQA) is becoming popular. However, their performance is still limited due to very little exposure to the LF during pre-training of LLMs, resulting in many syntac…
Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA , a benchmark designed to assess LMs’ understanding of traditional sports across 60 countries and 6 continents, encom
Line Probe Assay (LPA) is a widely used method for diagnosing drug-resistant tuberculosis (DRTB), but it is a time-consuming and labor-intensive process that requires expert interpretation. DRTB is a significant threat to global TB control efforts and its prompt diagnosis is critical for initiating…
Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved tr…
Semantic Parsing of natural language questions into their executable logical form (LF) has shown state-of-the-art (SOTA) performance for Knowledge Graph Question Answering (KGQA). However, these methods are not applicable for real-world applications, due to lack of KG-specific training data. Recent…
Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the d…
We present an end-to-end solution for recognizing merchandise displayed in the shelves of a supermarket. Given images of individual products, which are taken under ideal illumination for product marketing, the challenge is to find these products automatically in the images of the shelves. Note that…