arXiv subjects

Badri N. Patro

Publications and source records attributed to Badri N. Patro.

24 records · Page 2Linked to original sources

arXiv2019

Multimodal Differential Network for Visual Question Generation

Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).

cs.CL↗

arXiv2019

U-CAM: Visual Explanation using Uncertainty based Class Activation Maps

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanation for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.

cs.CV↗

arXiv2019

Probabilistic framework for solving Visual Dialog

In this paper, we propose a probabilistic framework for solving the task of `Visual Dialog'. Solving this task requires reasoning and understanding of visual modality, language modality, and common sense knowledge to answer. Various architectures have been proposed to solve this task by variants of multi-modal deep learning techniques that combine visual and language representations. However, we believe that it is crucial to understand and analyze the sources of uncertainty for solving this task. Our approach allows for estimating uncertainty and also aids a diverse generation of answers. The proposed approach is obtained through a probabilistic representation module that provides us with representations for image, question and conversation history, a module that ensures that diverse latent representations for candidate answers are obtained given the probabilistic representations and an uncertainty representation module that chooses the appropriate answer that minimizes uncertainty. We thoroughly evaluate the model with a detailed ablation analysis, comparison with state of the art and visualization of the uncertainty that aids in the understanding of the method. Using the proposed probabilistic framework, we thus obtain an improved visual dialog system that is also more explainable.

cs.CV↗

arXiv2019

Dynamic Attention Networks for Task Oriented Grounding

In order to successfully perform tasks specified by natural language instructions, an artificial agent operating in a visual world needs to map words, concepts, and actions from the instruction to visual elements in its environment. This association is termed as Task-Oriented Grounding. In this work, we propose a novel Dynamic Attention Network architecture for the efficient multi-modal fusion of text and visual representations which can generate a robust definition of state for the policy learner. Our model assumes no prior knowledge from visual and textual domains and is an end to end trainable. For a 3D visual world where the observation changes continuously, the attention on the visual elements tends to be highly co-related from a one-time step to the next. We term this as "Dynamic Attention". In this work, we show that Dynamic Attention helps in achieving grounding and also aids in the policy learning objective. Since most practical robotic applications take place in the real world where the observation space is continuous, our framework can be used as a generalized multi-modal fusion unit for robotic control through natural language. We show the effectiveness of using 1D convolution over Gated Attention Hadamard product on the rate of convergence of the network. We demonstrate that the cell-state of a Long Short Term Memory (LSTM) is a natural choice for modeling Dynamic Attention and shows through visualization that the generated attention is very close to how humans tend to focus on the environment.

cs.CV↗

arXiv2019

Granular Multimodal Attention Networks for Visual Dialog

Vision and language tasks have benefited from attention. There have been a number of different attention models proposed. However, the scale at which attention needs to be applied has not been well examined. Particularly, in this work, we propose a new method Granular Multi-modal Attention, where we aim to particularly address the question of the right granularity at which one needs to attend while solving the Visual Dialog task. The proposed method shows improvement in both image and text attention networks. We then propose a granular Multi-modal Attention network that jointly attends on the image and text granules and shows the best performance. With this work, we observe that obtaining granular attention and doing exhaustive Multi-modal Attention appears to be the best way to attend while solving visual dialog.

cs.CV↗

arXiv2019

Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.

cs.CL↗