La inteligencia artificial ha avanzado de manera vertiginosa en los últimos años, pero aún persiste un desafío fundamental: garantizar que los modelos no solo respondan preguntas complejas, sino que lo hagan con evidencia visual verificable. En este contexto nace E-VQA (Evidence-Backed Video Question Answering), una nueva tarea que exige a los sistemas de video comprensión ofrecer respuestas semánticas acompañadas de segmentos temporales y máscaras de segmentación densas y rastreadas. Frente a los enfoques tradicionales de
E-VQA: Evidence-Backed Video Question Answering
Explore E-VQA, a novel task that outputs semantic answers and precise spatio-temporal evidence. Learn about the ST-Evidence benchmark and scalable dataset.
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