Austin Waters

Austin Waters

Authored Publications
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    Preview abstract We explore the boundaries of scaling up a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix. View details
    Preview abstract Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pre-training on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 degree panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky, a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale training on near-human quality synthetic instructions. View details
    Simple and Effective Synthesis of Indoor 3D Scenes
    Jing Yu Koh
    Harsh Agrawal
    Dhruv Batra
    Honglak Lee
    Yinfei Yang
    Peter Anderson
    AAAI(2023) (to appear)
    Preview abstract We study the problem of synthesizing immersive 3D indoor scenes from one or a few images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A visionand-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the mature R2R benchmark. Our code is publicly released to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks. View details
    Preview abstract We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes. Existing generators suffer from poor visual grounding, causing them to rely on language priors and hallucinate objects. Our MARKY-MT5 system addresses this by focusing on visual landmarks; it comprises a first stage landmark detector and a second stage generator -- a multimodal, multilingual, multitask encoder-decoder. To train it, we bootstrap grounded landmark annotations on top of the Room-across-Room (RxR) dataset. Using text parsers, weak supervision from RxR's pose traces, and a multilingual image-text encoder trained on 1.8b images, we identify 1.1m English, Hindi and Telugu landmark descriptions and ground them to specific regions in panoramas. On Room-to-Room, human wayfinders obtain success rates (SR) of 71% following MARKY-MT5's instructions, just shy of their 75% SR following human instructions -- and well above SRs with other generators. Evaluations on RxR's longer, diverse paths obtain 61-64% SRs on three languages. Generating such high-quality navigation instructions in novel environments is a step towards conversational navigation tools and could facilitate larger-scale training of instruction-following agents. View details
    Leveraging Language ID in Multilingual End-to-End Speech Recognition
    Delia Qu
    Pedro Jose Moreno Mengibar
    IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019(2019)
    Preview abstract Multilingual speech recognition models are capable of recognizing speech in multiple different languages. Depending on the amount of training data, and the relatedness of languages, these models can outperform their monolingual counterparts. However, the performance of these models heavily relies on an externally provided language-id which is used to augment the input features or modulate the neural network's per-layer outputs using a language-gate. In this paper, we introduce a novel technique for inferring the language-id in a streaming fashion using the RNN-T loss that eliminates reliance on knowing the utterance's language. We conduct experiments on two sets of languages, arabic and nordic, and show the effectiveness of our approach. View details
    Preview abstract Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to text, and a natural language understanding module that transforms the resulting text (or top N hypotheses) into a set of intents and arguments. These modules are typically optimized independently. In this paper, we formulate audio to semantic understanding as a sequence-to-sequence problem. We propose and compare various encoder-decoder based approaches that optimizes both modules jointly, in an end-to-end manner. We evaluate these methods on a real-world task. Our results show that having an intermediate text representation while jointly optimizing the full system improves accuracy of prediction. View details
    Preview abstract Speech recognition systems that combine multiple types of acoustic models have been shown to outperform single-model systems. However, such systems can be complex to implement and too resource-intensive to use in production. This paper describes how to use knowledge distillation to combine acoustic models in a way that has the best of all worlds: It improves recognition accuracy significantly, can be implemented with standard training tools, and requires no additional complexity during recognition. First, we identify a simple but particularly strong type of ensemble: a late combination of recurrent neural networks with different architectures and training objectives. To harness such an ensemble, we use a variant of standard cross-entropy training to distill it into a single model and then discriminatively fine-tune the result. An evaluation on 2,000-hour large vocabulary tasks in 5 languages shows that the distilled models provide up to 8.9% WER improvement over conventionally-trained baselines, despite having an identical number of parameters. View details
    Towards Acoustic Model Unification Across Dialects
    Meysam Bastani
    Mohamed G. Elfeky
    Pedro Moreno
    2016 IEEE Workshop on Spoken Language Technology
    Preview abstract Research has shown that acoustic model performance typically decreases when evaluated on a dialectal variation of the same language that was not used during training. Similarly, models simultaneously trained on a group of dialects tend to under-perform when compared to dialect-specific models. In this paper, we report on our efforts towards building a unified acoustic model that can serve a multi-dialectal language. Two techniques are presented: Distillation and MTL. In Distillation, we use an ensemble of dialect-specific acoustic models and distill its knowledge in a single model. In MTL, we utilize MultiTask Learning to train a unified acoustic model that learns to distinguish dialects as a side task. We show that both techniques are superior to the naive model that is trained on all dialectal data, reducing word error rates by 4.2% and 0.6%, respectively. And, while achieving this improvement, neither technique degrades the performance of the dialect-specific models by more than 3.4%. View details