Pre-trained vision–language models (VLMs), such as CLIP, have become a strong foundation for few-shot learning. Despite this success, most existing adaptation methods restrict cross-modal interaction to a post-hoc stage, where visual and semantic representations are extracted independently and fused only at the logit level. When semantic guidance is applied only at the decision stage, the underlying feature space remains largely unchanged, limiting effective task-specific adaptation. To address this limitation, we propose Dynamic Cross-Modal Interplay (DCI), which advances cross-modal interaction from reactive fusion to proactive representation construction. DCI introduces Semantic-Guided Visual Calibration (SGVC), which conditions visual representation construction on instance-adaptive semantic anchors, enabling semantic priors to directly modulate and refine feature geometry. To ensure stable and balanced cross-modal interaction, we further incorporate Structure-Aware Regularization (SAR), which regularizes the calibration process by preserving inter-sample relational geometry and visual discriminability. Extensive experiments on several widely used benchmarks demonstrate consistent improvements over state-of-the-art methods. Further analysis shows that improvements are concentrated on semantically ambiguous samples.

Digital maps with lane-level details are the foundation of many applications. However, creating and maintaining digital maps especially maps with lane-level details, are labor-intensive and expensive. In this work, we propose a mapping pipeline to extract lane-level street maps from aerial imagery automatically. Our mapping pipeline first extracts lanes at non-intersection areas, then it enumerates all the possible turning lanes at intersections, validates the connectivity of them, and extracts the valid turning lanes to complete the map. We evaluate the accuracy of our mapping pipeline on a dataset consisting of four U.S. cities, demonstrating the effectiveness of our proposed mapping pipeline and the potential of scalable mapping solutions based on aerial imagery.

Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creating entirely new maps), but do not address the second step of leveraging this extracted information to update the existing map data. In this paper, we first explain why current map extraction techniques yield low accuracy when extended to update existing maps. We then propose a novel method that leverages the progression of satellite imagery over time to substantially improve accuracy. Our approach first compares satellite images captured at different times to identify portions of the physical road network that have visibly changed, and then updates the existing map accordingly. We show that our change-based approach reduces error rates four-fold.

Traffic accidents cost about 3% of the world’s GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. We present a technique to generate high-resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse and risk estimation is limited by bias-variance trade-off. Prior accident risk maps either estimate low-resolution maps that are of low utility (high bias), or they use frequency-based estimation techniques that inaccurately predict where accidents actually happen (high variance). To improve this trade-off, we use an end-to-end deep architecture that can input satellite imagery, GPS trajectories, road maps and the history of accidents. Our evaluation on four metropolitan areas in the US with a total area of 7,488 km2shows that our technique outperform prior work in terms of resolution and accuracy.

In this paper, we propose a self-supervised learning procedure for training a robust multi-object tracking (MOT) model given only unlabeled video. While several self-supervisory learning signals have been proposed in prior work on single-object tracking, such as color propagation and cycle-consistency, these signals are not effective for training RNN models, which are needed to achieve accurate MOT: they yield degenerate models that, for instance, always match new detections to tracks with the closest initial detections. We propose a novel self-supervisory signal that we call cross-input consistency: we construct two distinct inputs for the same sequence of video, by hiding different information about the sequence in each input. We then compute tracks in that sequence by applying an RNN model independently on each input, and train the model to produce consistent tracks across the two inputs. We evaluate our unsupervised method on MOT17 and KITTI --- remarkably, we find that, despite training only on unlabeled video, our unsupervised approach outperforms four supervised methods published in the last 1--2 years, including Tracktor++, FAMNet, GSM, and mmMOT.

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation – the limited effective receptive field of image classifiers.To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. Using a GNN allows information to propagate on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km2 area in 20 U.S. cities and a synthesized dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. In addition, RoadTagger is robust to disruptions in the satellite imagery and is able to learn complicated inductive rules for aggregating scattered information along the road network.

The rapid development of small aerial drones has enabled numerous drone-based applications, e.g., geographic mapping, air pollution sensing, and search and rescue. To assist the development of these applications, we propose BeeCluster, a drone orchestration system that manages a fleet of drones. BeeCluster provides a virtual drone abstraction that enables developers to express a sequence of geographical sensing tasks, and determines how to map these tasks to the fleet efficiently. BeeCluster's core contribution is predictive optimization, in which an inferred model of the future tasks of the application is used to generate an optimized flight and sensing schedule for the drones that aims to minimize the total expected execution time.

Video databases that enable queries with object-track predicates are useful in many applications. Such queries include selecting objects that move from one region of the camera frame to another (e.g., finding cars that turn right through a junction) and selecting objects with certain speeds (e.g., finding animals that stop to drink water from a lake). Processing such predicates efficiently is challenging because they involve the movement of an object over several video frames. We propose a novel query-driven tracking approach that integrates query processing with object tracking to efficiently process object track queries and address the computational complexity of object detection methods. By processing video at low framerates when possible, but increasing the framerate when needed to ensure high-accuracy on a query, our approach substantially speeds up query execution. We have implemented query-driven tracking in MIRIS, a video query processor, and compare MIRIS against four baselines on a diverse dataset consisting of five sources of video and nine distinct queries. We find that, at the same accuracy, MIRIS accelerates video query processing by 9x on average over the IOU tracker, an overlap-based tracking-by-detection method used in existing video database systems.

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems. Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.

Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities.

Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.

Current approaches to construct road network maps from GPS trajectories suffer from low precision, especially in dense urban areas and in regions with complex topologies such as overpasses and underpasses, parallel roads, and stacked roads. This paper proposes a two-stage method to improve precision without sacrificing recall (coverage). The first stage, RoadRunner, is a method that can generate high-precision maps even in challenging scenarios by incrementally following the flow of trajectories, using the connectivity between observations in each trajectory to decide whether overlapping trajectories are traversing the same road or distinct parallel roads, and to correctly infer road segment connectivity. By itself, RoadRunner is not designed to achieve high recall, but we show how to combine it with a wide range of prior schemes, some that use GPS trajectories and some that use aerial imagery, to achieve recall similar to prior schemes but at substantially higher precision. We evaluated RoadRunner in four U.S. cities using 60,000 GPS trajectories, and found that precision improves by 5.2 points (a 33.6% error rate reduction) and 24.3 points (a 60.7% error rate reduction) over two existing schemes, with a slight increase in recall.

Drawing or dragging an object on a mobile device is annoying today because the latency is manifested spatially with an obvious gap between the touch point and the line head or dragged object. This work identifies the multiple synchronization points in the input to display path of modern mobile systems as a major source of latency, contributing about 30 ms to the overall latency.

The extremely-high display density of modern smartphones imposes a significant burden on power consumption, yet does not always provide an improved user experience and may even lead to a compromised user experience. As human visually-perceivable ability highly depends on the user-screen distance, a reduced display resolution may still achieve the same user experience when the user-screen distance is large. This provides new power-saving opportunities. In this paper, we present a flexible dynamic resolution scaling system for smartphones. The system adopts an ultrasonic-based approach to accurately detect the user-screen distance at low-power cost and makes scaling decisions automatically for maximum user experience and power saving. App developers or users can also adjust the resolution manually as their needs. Our system is able to work on existing commercial smartphones and support legacy apps, without requiring re-building the ROM or any changes of apps. An end-to-end dynamic resolution scaling system is implemented on the Galaxy S5 LTE-A and Nexus 6 smartphones, and the correctness and effectiveness are evaluated against 30 games and benchmarks. Experimental results show that all the 30 apps can run successfully with per-frame, real-time dynamic resolution scaling. The energy per frame can be reduced by 30.1% on average and up to 60.5\% at most when the resolution is halved, for 15 apps. A user study with 10 users indicates that our system remains good user experience, as none of the 10 users could perceive the resolution changes in the user study.

The extremely-high display density of modern smartphones imposes a significant burden on power consumption, yet does not always provide an improved user experience and may even lead to a compromised user experience. As human visually-perceivable ability highly depends on the user-screen distance, a reduced display resolution may still achieve the same user experience when the user-screen distance is large. This provides new power-saving opportunities. We present a flexible dynamic resolution scaling system for smartphones. The system adopts an ultrasonic-based approach to detect the user-screen distance at low-power cost and makes scaling decisions automatically for maximum user experience and power saving. App developers or users can also adjust the resolution manually and dynamically as their needs. Our system is able to work on the existing commercial smartphones and support the legacy apps, without requiring re-building the ROM or any changes from apps.