Abhishek Das

TL;DR: This work combines existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and applies it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures.

TL;DR: Grad-CAM as mentioned in this paper uses the gradients of any target concept (e.g., a dog in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept.

TL;DR: It is shown that Guided Grad-CAM helps untrained users successfully discern a "stronger" deep network from a "weaker" one even when both networks make identical predictions, and also exposes the somewhat surprising insight that common CNN + LSTM models can be good at localizing discriminative input image regions despite not being trained on grounded image-text pairs.

TL;DR: In this article, the authors introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content, given an image, a dialog history and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately.

TL;DR: The authors introduced the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content, given an image, a dialog history and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately.