Efficient Attention Mechanism for Visual Dialog that Can Handle All the Interactions Between Multiple Inputs

Nguyễn, Văn Quảng; Suganuma, Masanori; Okatani, Takayuki

doi:10.1007/978-3-030-58586-0_14

Cited by 38 publications

(27 citation statements)

References 35 publications

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“…A preliminary version of the method won the ALFRED Challenge 2020 1 . The present version further improved the task success rate in unseen and seen environments to 8.37% and 29.16%, respectively, which are significantly higher than the previously published SOTA (0.39% and 3.98%, respectively) [Shridhar et al, 2020].…”

Section: Introductionmentioning

confidence: 48%

“…Several variants of VLN tasks have been proposed. A study [Nguyen et al, 2019] allows the agent to communicate with an adviser using natural language to accomplish a given goal. In a study , the agent placed in an environment attempts to find a specified object by communicating with a human by natural language dialog.…”

Section: Embodied Vision-language Tasksmentioning

confidence: 99%

“…To consider more complex tasks, a benchmark named AL-FRED was developed recently [Shridhar et al, 2020]. It requires an agent to accomplish a household task in interactive environments following given language directives.…”

Section: Introductionmentioning

confidence: 99%

“…The agent also needs to (3) localize the objects to manipulate by predicting the pixel-wise masks. Previous studies (e.g., [Shridhar et al, 2020]) employ a Seq2Seq model, which performs well on the VLN tasks [Ma et al, 2019]. However, it works poorly on ALFRED.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, we propose to choose a single instruction to process at each timestep from the given series of instructions. This approach contrasts with previous methods that encode them into a single long sequence of word features and use soft attention to specify which instruction to consider at each timestep implicitly [Shridhar et al, 2020;Singh et al, 2020a]. Our method chooses individual instructions explicitly by learning to predict when the agent completes an instruction.…”

Section: Introductionmentioning

confidence: 99%

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Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks

Nguyen

Suganuma

Okatani

2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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There is a growing interest in the community in making an embodied AI agent perform a complicated task while interacting with an environment following natural language directives. Recent studies have tackled the problem using ALFRED, a well-designed dataset for the task, but achieved only very low accuracy. This paper proposes a new method, which outperforms the previous methods by a large margin. It is based on a combination of several new ideas. One is a two-stage interpretation of the provided instructions. The method first selects and interprets an instruction without using visual information, yielding a tentative action sequence prediction. It then integrates the prediction with the visual information etc., yielding the final prediction of an action and an object. As the object's class to interact is identified in the first stage, it can accurately select the correct object from the input image. Moreover, our method considers multiple egocentric views of the environment and extracts essential information by applying hierarchical attention conditioned on the current instruction. This contributes to the accurate prediction of actions for navigation. A preliminary version of the method won the ALFRED Challenge 2020. The current version achieves the unseen environment's success rate of 4.45% with a single view, which is further improved to 8.37% with multiple views.

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Section: Introductionmentioning

confidence: 48%

Section: Embodied Vision-language Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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