Map representations learned by expert demonstrations have shown promising research value. However, the field of visual navigation still faces challenges due to the lack of real-world human-navigation datasets that can support efficient, supervised, representation learning of environments. We present a Landmark-Aware Visual Navigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies and map building. We collect RGBD observation and human point-click pairs as a human annotator explores virtual and real-world environments with the goal of full coverage exploration of the space. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. Our dataset covers a wide spectrum of scenes, including rooms in indoor environments, as well as walkways outdoors. We releaseour dataset with detailed documentation at https://huggingface.co/datasets/visnavdataset/lavn (DOI: l0.57967/hf/2386) and a plan for long-term preservation.

We propose Few-Class Arena (FCA), as a unified benchmark with focus on testing efficient image classification models for few classes. A wide variety of benchmark datasets with many classes (80-1000) have been created to assist Computer Vision architectural evolution. An increasing number of vision models are evaluated with these many-class datasets. However, real-world applications often involve substantially fewer classes of interest (2-10). This gap between many and few classes makes it difficult to predict performance of the few-class applications using models trained on the available many-class datasets. To date, little has been offered to evaluate models in this Few-Class Regime. We conduct a systematic evaluation of the ResNet family trained on ImageNet subsets from 2 to 1000 classes, and test a wide spectrum of Convolutional Neural Networks and Transformer architectures over ten datasets by using our newly proposed FCA tool. Furthermore, to aid an up-front assessment of dataset difficulty and a more efficient selection of models, we incorporate a difficulty measure as a function of class similarity. FCA offers a new tool for efficient machine learning in the Few-Class Regime, with goals ranging from a new efficient class similarity proposal, to lightweight model architecture design, to a new scaling law. FCA is user-friendly and can be easily extended to new models and datasets, facilitating future research work. Our benchmark is available at https://github.com/bryanbocao/fca.

Millions of video cameras are deployed globally across major cities for learning-based video analytic (VA) applications, such as object detection. Video streams from the cameras are either sent over the wide-area network to be processed by the cloud or are (at least partially) processed in a local edge workstation, incurring significant latency and elevated financial costs. In this paper, to minimize reliance on the cloud and overcome the unavailability of high-compute workstations on edge, we investigate the use of heterogeneous and distributed embedded devices as edge nodes shared by multiple cameras to fully serve the video processing needs of a VA application (without requiring cloud support). We present OVIDA, an edge-only orchestrator to deploy VA application(s) on a distributed edge environment to maximize accuracy. Given the resource-constrained nature of edge nodes, OVIDA disaggregates the VA application pipeline into multiple modules. OVIDA's core functionality and contributions are: (i) optimizing the placement and replication of the VA application modules across the edge nodes to maximize the throughput, and in turn, accuracy; and (ii) an adaptive model selection algorithm for VA modules based on accuracy-throughput tradeoff to maximize accuracy in response to varying load conditions. To further improve performance, OVIDA employs a central-queue-based design (instead of the usual push-based design), which also obviates the need for complex load balancing algorithms. We implement OVIDA on top of Kubernetes and evaluate its performance for three VA applications, supported over a heterogeneous edge cluster under varying network conditions. When compared against several baselines in our evaluation, we achieve throughput and accuracy gains of at least 51% and 28%.

Edge computing has emerged as an alternative to reduce transmission and processing delay and preserve privacy of the video streams. However, the ever-increasing complexity of Deep Neural Networks (DNNs) used in video-based applications (e.g. object detection) exerts pressure on memory-constrained edge devices. Model merging is proposed to reduce the DNNs' memory footprint by keeping only one copy of merged layers' weights in memory. In existing model merging techniques, (i) only architecturally identical layers can be shared; (ii) requires computationally expensive retraining in the cloud; (iii) assumes the availability of ground truth for retraining. The re-evaluation of a merged model's performance, however, requires a validation dataset with ground truth, typically runs at the cloud. Common metrics to guide the selection of shared layers include the size or computational cost of shared layers or representation size. We propose a new model merging scheme by sharing representations (i.e., outputs of layers) at the edge, guided by representation similarity S. We show that S is extremely highly correlated with merged model's accuracy with Pearson Correlation Coefficient |r| > 0.94 than other metrics, demonstrating that representation similarity can serve as a strong validation accuracy indicator without ground truth. We present our preliminary results of the newly proposed model merging scheme with identified challenges, demonstrating a promising research future direction.

In this paper, we present Vi-Fi, a multi-modal system that leverages a user's smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.

In this paper, we present ViTag to associate user identities across multimodal data, particularly those obtained from cameras and smartphones. ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Mea-surement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones. We formulate the problem as association by sequence to sequence (seq2seq) translation. In this two-step process, our system first performs cross-modal translation using a multimodal LSTM encoder-decoder network (X-Translator) that translates one modality to another, e.g. recon-structing IMU and FTM readings purely from camera bounding boxes. Second, an association module finds identity matches between camera and phone domains, where the translated modality is then matched with the observed data from the same modality. In contrast to existing works, our proposed approach can associate identities in multi-person scenarios where all users may be performing the same activity. Extensive experiments in real-world indoor and outdoor environments demonstrate that online association on camera and phone data (IMU and FTM) achieves an average Identity Precision Accuracy (IDP) of 88.39% on a 1 to 3 seconds window, outperforming the state-of-the-art Vi-Fi (82.93%). Further study on modalities within the phone domain shows the FTM can improve association performance by 12.56% on average. Finally, results from our sensitivity experiments demonstrate the robustness of ViTag under different noise and environment variations.