Dhruv Batra

TL;DR: The results demonstrate that 1) the model learns interpretable decision states in an unsupervised manner, and 2) these learned decision states transfer to goal-driven tasks in new environments, effectively guide exploration, and improve performance.

TL;DR: The papers in this special section address the programs and services supported by graphical models in computer vision and the aspects of modeling novel priors, inference algorithms and parameter learning methods in the context of higher order graphical models.

TL;DR: A gradient-based interpretability approach is presented to determine the questions most strongly correlated with the reasoning question on an image, and a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an pair is proposed.

TL;DR: This paper proposes using group-sparsity-inducing regularizers such as ℓ1-ℓ2 to estimate the parameters of Structured SVMs with unstructured latent variables to regularize the complexity of the latent space and learn which hidden states are really relevant for prediction.

TL;DR: The authors proposed a contrastive loss to encourage representations to be robust to linguistic variations in questions while the cross-entropy loss preserves the discriminative power of representations for answer prediction and showed that optimizing both losses is key to effective training.