Papers by Vihan Jain
Learning to Represent Image and Text with Denotation Graph (2020.emnlp-main)
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| Challenge: | Recent advances in learning representations of visual and language information have been a problem with many applications. |
| Approach: | They propose to extract visual expressions from images aligned with linguistic expressions that describe the images to learn representations from implicit expressions. |
| Outcome: | The proposed representations lead to stronger empirical results on downstream tasks of cross-modal image retrieval, referring expression, and compositional attribute-object recognition. |
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps (2020.acl-main)
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| Challenge: | Existing state-of-the-art VLN agents do not generalize well for long navigation tasks. |
| Approach: | They propose a VLN agent that is learned to navigate by decomposing long instructions into shorter ones and completing them sequentially. |
| Outcome: | The proposed agent can follow long instructions better than existing ones, but it does not generalize well. |
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation (P19-1)
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| Challenge: | Existing metrics for vision-and-language navigation focus on goal completion rather than the sequence of actions corresponding to the instructions. |
| Approach: | They propose to use a room-to-room dataset to measure the length of instruction followed by agents. |
| Outcome: | The proposed metric outperforms existing metrics for Room-to-Room tasks because it is direct-to goal shortest. |
On the Evaluation of Vision-and-Language Navigation Instructions (2021.eacl-main)
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| Challenge: | Existing instruction generators have not been evaluated using human wayfinders . BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions. |
| Approach: | They propose an instruction-trajectory compatibility model that operates without reference instructions to improve wayfinding performance. |
| Outcome: | The proposed model shows the highest correlation with human wayfinding outcomes when scoring individual instructions. |