Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime. Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task. However, these approaches are prone to selecting highly redundant samples, since they fail to incorporate any notion of diversity. In our work, we first demonstrate that data selection strategies used in prior retrieval-augmented few-shot adaptation settings can be generalized using a class of functions known as Combinatorial Mutual Information (CMI) measures. We then propose COBRA (COmBinatorial Retrieval Augmentation), which employs an alternative CMI measure that considers both diversity and similarity to a target dataset. COBRA consistently outperforms previous retrieval approaches across image classification tasks and few-shot learning techniques when used to retrieve samples from LAION-2B. COBRA introduces negligible computational overhead to the cost of retrieval while providing significant gains in downstream model performance.

This paper explores a machine learning approach on data from a single-chip mmWave radar for generating high resolution point clouds – a key sensing primitive for robotic applications such as mapping, odometry and localization. Unlike lidar and vision-based systems, mmWave radar can operate in harsh environments and see through occlusions like smoke, fog, and dust. Unfortunately, current mmWave processing techniques offer poor spatial resolution compared to lidar point clouds. This paper presents RadarHD, an end-to-end neural network that constructs lidar-like point clouds from low resolution radar input. Enhancing radar images is challenging due to the presence of specular and spurious reflections. Radar data also doesn't map well to traditional image processing techniques due to the signal's sinc-like spreading pattern. We overcome these challenges by training RadarHD on a large volume of raw I/Q radar data paired with lidar point clouds across diverse indoor settings. Our experiments show the ability to generate rich point clouds even in scenes unobserved during training and in the presence of heavy smoke occlusion. Further, RadarHD's point clouds are high-quality enough to work with existing lidar odometry and mapping workflows.

Millimeter wave radars can perceive through occlusions like dust, fog, smoke and clothes. But compared to cameras and lidars, their perception quality is orders of magnitude poorer. RadarHD [3] tackles this problem of poor quality by creating a machine learning super resolution pipeline trained against high quality lidar scans to mimic lidar. RadarHD ingests low resolution radar and generates high quality lidar-like point clouds even in occluded settings. RadarHD can also make use of the high quality output for typical robotics tasks like odometry, mapping and classification using conventional lidar workflows. Here, we demonstrate the effectiveness of RadarHD's point clouds against lidar in occluded settings.

Continual learning is an emerging research challenge in machine learning that addresses the problem where models quickly fit the most recently trained-on data but suffer from catastrophic forgetting of previous data due to distribution shifts --- it does this by maintaining a small historical replay buffer in replay-based methods. To avoid these problems, this paper proposes a method, ``Retrospective Adversarial Replay (RAR)'', that synthesizes adversarial samples near the forgetting boundary. RAR perturbs a buffered sample towards its nearest neighbor drawn from the current task in a latent representation space. By replaying such samples, we are able to refine the boundary between previous and current tasks, hence combating forgetting and reducing bias towards the current task. To mitigate the severity of a small replay buffer, we develop a novel MixUp-based strategy to increase replay variation by replaying mixed augmentations. Combined with RAR, this achieves a holistic framework that helps to alleviate catastrophic forgetting. We show that this excels on broadly-used benchmarks and outperforms other continual learning baselines especially when only a small buffer is available. We conduct a thorough ablation study over each key component as well as a hyperparameter sensitivity analysis to demonstrate the effectiveness and robustness of RAR.

As an extension to the matroid span problem, we propose the submodular span problem that involves finding a large set of elements with small gain relative to a given query set. We then propose a two-stage Submodular Span Summarization (S3) framework to achieve a form of conditional or query-focused data summarization. The first stage encourages the summary to be relevant to a given query set, and the second stage encourages the final summary to be diverse, thus achieving two important necessities for a good query-focused summary. Unlike previous methods, our framework uses only a single submodular function defined over both data and query. We analyze theoretical properties in the context of both matroids and polymatroids that elucidate when our methods should work well. We find that a scalable approximation algorithm to the polymatroid submodular span problem has good theoretical and empirical properties. We provide empirical and qualitative results on three real-world tasks: conditional multi-document summarization on the DUC 2005-2007 datasets, conditional video summarization on the UT-Egocentric dataset, and conditional image corpus summarization on the ImageNet dataset. We use deep neural networks, specifically a BERT model for text, AlexNet for video frames, and Bi-directional Generative Adversarial Networks (BiGAN) for ImageNet images to help instantiate the submodular functions. The result is a minimally supervised form of conditional summarization that matches or improves over the previous state-of-the-art.