Summary of Aigen: An Adversarial Approach For Instruction Generation in Vln, by Niyati Rawal et al.
AIGeN: An Adversarial Approach for Instruction Generation in VLN
by Niyati Rawal, Roberto Bigazzi, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed architecture, AIGeN, leverages Generative Adversarial Networks (GANs) to produce synthetic instructions that improve navigation agents’ performance in Vision-and-Language Navigation (VLN). This medium-difficulty summary assumes a technical audience familiar with machine learning but not necessarily specialized in the VLN subfield. The paper proposes a novel architecture combining a Transformer decoder (GPT-2) and encoder (BERT) to generate meaningful synthetic instructions, improving navigation agents’ performance. The model is trained on Habitat-Matterport 3D Dataset (HM3D), and results demonstrate an improvement in off-the-shelf VLN method performance. AIGeN outperforms state-of-the-art models on REVERIE and R2R benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AIGeN is a new way to create fake instructions that help navigation agents get better at following human directions. It’s like a game where the agent has to find its way to a specific spot based on what someone says. The team behind AIGeN used special computer models, like GPT-2 and BERT, to come up with new sentences that are like real instructions. They tested it on big datasets and showed that their method works better than others in this area. |
Keywords
» Artificial intelligence » Bert » Decoder » Encoder » Gpt » Machine learning » Transformer
