Products

AI Research

Resources

About

Overview
Open Source
Careers

RESEARCH

NLP

Do Explanations Make VQA Models More Predictable To A Human?

November 02, 2018

Abstract

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model – its responses as well as failures – more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.

Download the Paper

AUTHORS

Written by

Devi Parikh

Arjun Chandrasekaran

Deshraj Yadav

Prithvijit Chattopadhyay

Viraj Prabhu

Publisher

EMNLP

Research Topics

Natural Language Processing (NLP)

Computer Vision

Related Publications

June 05, 2026

CONVERSATIONAL AI

RANKING AND RECOMMENDATIONS

Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval

Anshumali Shrivastava, Jason Chen, Qi Ma, Zeyu Yang

June 05, 2026

Read the Paper

May 26, 2026

HUMAN & MACHINE INTELLIGENCE

THEORY

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images

Valentin Wyart, Huy V. Vo, Jean Remi King, Josephine Raugel, Jérémy Rapin, Marc Szafraniec, Max Seitzer, Patrick Labatut, Piotr Bojanowski

May 26, 2026

Read the Paper

May 20, 2026

HUMAN & MACHINE INTELLIGENCE

RESEARCH

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data

Alvin W. M. Tan, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Michael C. Frank, Angel Villar Corrales, Charles-Eric Saint-James, Dongyan Lin, Emmanuel Dupoux, Jiayi Shen, Juan Pino, Mahi Luthra, Martin Gleize, Phillip Rust, Rashel Moritz, Sheila Krogh-Jespersen, Surya Parimi, Tom Fizycki, Vanessa Stark, Yosuke Higuchi, Youssef Benchekroun