Sanchit Tanwar | Searcharxiv

arXiv2026

Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving

Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the known phenomenon that synthetically generated MCQAs are highly susceptible to hidden textual cues that allow models to exploit linguistic patterns rather than visual context. Our results show that a VLM fine-tuned on such data can achieve accuracy comparable to human-validated benchmarks even without visual input. Our proposed method reduces blind accuracy from +66.9% above random to +2.9%, eliminating the vast majority of exploitable textual shortcuts. By decoupling the correct answer from linguistic artifacts and employing a curriculum learning strategy, we force the model to rely on visual grounding, ensuring that performance accurately reflects perceptual understanding.

cs.LG↗

arXiv2021

Indian Licence Plate Dataset in the wild

Indian Licence Plate Detection is a problem that has not been explored much at an open-source level.There are proprietary solutions available for it, but there is no big open-source dataset that can be used to perform experiments and test different approaches.Most of the large datasets available are for countries like China, Brazil, but the model trained on these datasets does not perform well on Indian plates because the font styles and plate designs used vary significantly from country to country.This paper introduces an Indian license plate dataset with 16192 images and 21683 plate plates annotated with 4 points for each plate and each character in the corresponding plate.We present a benchmark model that uses semantic segmentation to solve number plate detection. We propose a two-stage approach in which the first stage is for localizing the plate, and the second stage is to read the text in cropped plate image.We tested benchmark object detection and semantic segmentation model, for the second stage, we used lprnet based OCR.

cs.CV↗