Ondrej Miksik

Benoit Guillard, Sai Vemprala, Jayesh K. Gupta et al.

Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We propose a model that learns a mapping between RGB images and corresponding LiDAR features such as raydrop or per-point intensities directly from real datasets. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces or high intensity returns on reflective materials. When applied to naively raycasted point clouds provided by off-the-shelf simulator software, our model enhances the data by predicting intensities and removing points based on the scene's appearance to match a real LiDAR sensor. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly. Through a sample task of vehicle segmentation, we show that enhancing simulated point clouds with our technique improves downstream task performance.

Madeline Chantry Schiappa, Shehreen Azad, Sachidanand VS et al.

Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of multi-modal data using self-supervised or semi-supervised learning have emerged. These ``foundation'' models are often adapted to a variety of downstream tasks like classification, object detection, and segmentation with little-to-no training on the target dataset. In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks and focus on robustness against real-world distribution shift inspired perturbations. We benchmark seven state-of-the-art segmentation architectures using 2 different perturbed datasets, MS COCO-P and ADE20K-P, with 17 different perturbations with 5 severity levels each. Our findings reveal several key insights: (1) VFMs exhibit vulnerabilities to compression-induced corruptions, (2) despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and (3) VFMs demonstrate enhanced robustness for certain object categories. These observations suggest that our robustness evaluation framework sets new requirements for foundational models, encouraging further advancements to bolster their adaptability and performance. The code and dataset is available at: \url{https://tinyurl.com/fm-robust}.

O. Miksik, I. Munasinghe, J. Asensio-Cubero et al.

Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other words, interactions have to be initiated by the user, which somewhat limits their usability and user experience. We propose, that the next generation of such devices should be able to proactively provide the right information in the right way at the right time, without being prompted by the user. However, achieving this is not straightforward, since there is the danger it could interrupt what the user is doing too much, resulting in it being distracting or even annoying. Furthermore, it could unwittingly, reveal sensitive/private information to third parties. In this report, we discuss the challenges of developing proactively initiated interactions, and suggest a framework for when it is appropriate for the device to intervene. To validate our design assumptions, we describe firstly, how we built a functioning prototype and secondly, a user study that was conducted to assess users' reactions and reflections when in the presence of a proactive voice assistant. This pre-print summarises the state, ideas and progress towards a proactive device as of autumn 2018.

Pawel Swietojanski, Ondrej Miksik

As we interact with the world, for example when we communicate with our colleagues in a large open space or meeting room, we continuously analyse the surrounding environment and, in particular, localise and recognise acoustic events. While we largely take such abilities for granted, they represent a challenging problem for current robots or smart voice assistants as they can be easily fooled by high degree of sound interference in acoustically complex environments. Preventing such failures when using solely audio data is challenging, if not impossible since the algorithms need to take into account wider context and often understand the scene on a semantic level. In this paper, we propose what to our knowledge is the first multi-modal direction of arrival (DoA) of sound, which uses static visual spatial prior providing an auxiliary information about the environment to suppress some of the false DoA detections. We validate our approach on a newly collected real-world dataset, and show that our approach consistently improves over classic DoA baselines

Ondrej Miksik, Vibhav Vineet

Standard 3D reconstruction pipelines assume stationary world, therefore suffer from `ghost artifacts' whenever dynamic objects are present in the scene. Recent approaches has started tackling this issue, however, they typically either only discard dynamic information, represent it using bounding boxes or per-frame depth or rely on approaches that are inherently slow and not suitable to online settings. We propose an end-to-end system for live reconstruction of large-scale outdoor dynamic environments. We leverage recent advances in computationally efficient data-driven approaches for 6-DoF object pose estimation to segment the scene into objects and stationary `background'. This allows us to represent the scene using a time-dependent (dynamic) map, in which each object is explicitly represented as a separate instance and reconstructed in its own volume. For each time step, our dynamic map maintains a relative pose of each volume with respect to the stationary background. Our system operates in incremental manner which is essential for on-line reconstruction, handles large-scale environments with objects at large distances and runs in (near) real-time. We demonstrate the efficacy of our approach on the KITTI dataset, and provide qualitative and quantitative results showing high-quality dense 3D reconstructions of a number of dynamic scenes.

Anurag Arnab, Ondrej Miksik, Philip H. S. Torr

Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness.

Shehroze Bhatti, Alban Desmaison, Ondrej Miksik et al.

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios. Inspired from prior work in human cognition that indicates how humans employ a variety of semantic concepts and abstractions (object categories, localisation, etc.) to reason about the world, we build an agent-model that incorporates such abstractions into its policy-learning framework. We augment the raw image input to a Deep Q-Learning Network (DQN), by adding details of objects and structural elements encountered, along with the agent's localisation. The different components are automatically extracted and composed into a topological representation using on-the-fly object detection and 3D-scene reconstruction.We evaluate the efficacy of our approach in Doom, a 3D first-person combat game that exhibits a number of challenges discussed, and show that our augmented framework consistently learns better, more effective policies.

Anurag Arnab, Michael Sapienza, Stuart Golodetz et al.

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials. In this paper, we therefore present an approach that augments the available dense visual cues with sparse auditory cues in order to estimate dense object and material labels. Since estimates of object class and material properties are mutually informative, we optimise our multi-output labelling jointly using a random-field framework. We evaluate our system on a new dataset with paired visual and auditory data that we make publicly available. We demonstrate that this joint estimation of object and material labels significantly outperforms the estimation of either category in isolation.