Papers by Alexander Ku

4 papers
Prompt Expansion for Adaptive Text-to-Image Generation (2024.acl-long)

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Challenge: Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the prompts can be repetitive.
Approach: They propose a framework that takes a text query as input and outputs a set of expanded text prompts that are optimized to generate a wider variety of appealing images.
Outcome: The proposed framework generates high-quality images from text prompts with less effort and is more aesthetically pleasing than baseline models.
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.
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding (2020.emnlp-main)

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Challenge: Room-Across-Room (RxR) is a vision-and-language navigation dataset that addresses gaps in existing ones by addressing known biases in paths and eliciting more references to visible entities.
Approach: They introduce a new Vision-and-Language Navigation (VLN) dataset that addresses biases in paths and elicits more references to visible entities.
Outcome: The proposed model learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations.
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.

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