Alexander Ku

Alexander Ku

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    Preview abstract Despite recent advancements, text-to-image (T2I) models still exhibit critical limitations, such as errors in understanding spatial relationships, object counting, text rendering, and more. One challenge in overcoming these failure modes is the lack of resources; the majority of existing image-text datasets provide only brief captions that do not offer sufficient detail to discrepancies between images and their descriptions. To advance the development of T2I models further, we introduce \textbf{Descriptions of Connected and Contrasting Images (DOCCI)}, a dataset of 15k images taken by a single person with detailed human-annotated descriptions in English. We meticulously annotated detailed and coherent descriptions, averaging 136 words, which sufficiently differentiate images from related or similar ones. We intentionally curated images that showcase a diverse range of visual properties, including entities with their attributes, various orientations, and lighting effects, many of which are related to each other. We thoroughly analyze the quality and characteristics of the image-description pairs, and assess the performance of the latest T2I and I2T models. The experimental results indicate that the current state-of-the-art T2I models still struggle with the aforementioned challenges, and even the SOTA models have not fully addressed them. DOCCI is publicly available, and we believe that this dataset will be a valuable benchmark for vision-language research. 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
    On the Evaluation of Vision-and-Language Navigation Instructions
    Ming Zhao
    Peter Anderson
    Vihan Jain
    Conference of the European Chapter of the Association for Computational Linguistics (EACL)(2021)
    Preview abstract Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions. However, existing instruction generators have not been comprehensively evaluated, and the automatic evaluation metrics used to develop them have not been validated. Using human wayfinders, we show that these generators perform on par with or only slightly better than a template-based generator and far worse than human instructors. Furthermore, we discover that BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions. To improve instruction evaluation, we propose an instruction-trajectory compatibility model that operates without reference instructions. Our model shows the highest correlation with human wayfinding outcomes when scoring individual instructions. For ranking instruction generation systems, if reference instructions are available we recommend using SPICE. View details
    PanGEA: The Panoramic Graph Environment Annotation Toolkit
    Peter Anderson
    2nd Workshop on Advances in Language and Vision Research (ALVR)(2021)
    Preview abstract PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen. It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators. Out of the box, PanGEA supports two tasks -- collecting navigation instructions and navigation instruction following -- and it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks. We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room (RxR) dataset. We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support. View details
    Preview abstract We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations. We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments. View details
    Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
    Vihan Jain
    Gabriel Magalhaes
    Ashish Vaswani
    Association for Computational Linguistics(2019)
    Preview abstract Advances in learning and representations have reinvigorated work that connects language to other modalities. A particularly exciting direction is Vision-and-Language Navigation (VLN), in which agents interpret natural language instructions and visual scenes to move through environments and reach goals. Despite recent progress, current research leaves unclear how much of a role language understanding plays in this task, especially because dominant evaluation metrics have focused on goal completion rather than the sequence of actions corresponding to the instructions. Here, we highlight shortcomings of current metrics for the Room-to-Room dataset (Anderson et al. 2018b) and propose a new metric, Coverage weighted by Length Score (CLS). We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths. We join existing short paths to form more challenging extended paths to create a new data set, Room-for-Room (R4R). Using R4R and CLS, we show that agents that receive rewards for instruction fidelity outperform agents that focus on goal completion. View details
    General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
    Gabriel Ilharco Magalhaes
    Vihan Jain
    NeurIPS Visually Grounded Interaction and Language (ViGIL) Workshop(2019)
    Preview abstract In instruction conditioned navigation, agents interpret natural language and their surroundings to navigate in an environment. Datasets for such tasks typically contain pairs of these instructions and reference trajectories, but current popular evaluation metrics fail to properly account for the fidelity of agents to the those trajectories. To address this, we introduce the normalized Dynamic Time Warping (nDTW) metric. nDTW softly penalizes deviations from the reference path, is naturally sensitive to the order of the nodes composing each path, is suited for both continuous and graph-based evaluations, and can be efficiently calculated. Further, we define SDTW, which constrains nDTW to only successful episodes and effectively captures both success and fidelity. We collect human similarity judgments for simulated paths and find our DTW metrics correlates better with human rankings than all other metrics. We also show that using nDTW as a reward signal for agents using reinforcement learning improves performance on both the Room-to-Room and Room-for-Room datasets. View details
    Preview abstract Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric. View details
    A universal SNP and small-indel variant caller using deep neural networks
    Scott Schwartz
    Dan Newburger
    Jojo Dijamco
    Nam Nguyen
    Pegah T. Afshar
    Sam S. Gross
    Lizzie Dorfman
    Mark A. DePristo
    Nature Biotechnology(2018)
    Preview abstract Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling. View details
    Image Transformer
    Niki J. Parmar
    Ashish Vaswani
    Jakob Uszkoreit
    Lukasz Kaiser
    Noam Shazeer
    Dustin Tran
    International Conference on Machine Learning (ICML)(2018)
    Preview abstract Recent work demonstrated significant progress towards modeling the distribution of natural images with tractable likelihood using deep neural networks. This was achieved by modeling the joint distribution of pixels in the image as the product of conditional distributions, thereby turning it into a sequence modeling problem, and applying recurrent or convolutional neural networks to it. In this work we instead build on the Transformer, a recently proposed network architecture based on self-attention, to model the conditional distributions in similar factorizations. We present two extensions of the network architecture, allowing it to scale to images and to take advantage of their two-dimensional structure. While conceptually simple, our generative models trained on two image data sets are competitive with or outperform the current state of the art on two different data sets, CIFAR-10 and ImageNet, as measured by log-likelihood. We also present results on image super-resolution with large magnification ratio with an encoder-decoder configuration of our architecture. In a human evaluation study, we show that our super-resolution models improve over previously published autoregressive super-resolution models in how often they fool a naive human observer by a factor of three. Lastly, we provide examples of images generated or completed by our various models which, following previous work, we also believe to look pretty cool. View details
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