The 5th AI City Challenge

The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems.

CE-FPN: Enhancing Channel Information for Object Detection

Feature pyramid network (FPN) has been an effective framework to extract multi-scale features in object detection. However, current FPN-based methods mostly suffer from the intrinsic flaw of channel reduction, which brings about the loss of semantical information. And the miscellaneous fused feature maps may cause serious aliasing effects. In this paper, we present a novel channel enhancement feature pyramid network (CE-FPN) with three simple yet effective modules to alleviate these problems. Specifically, inspired by sub-pixel convolution, we propose a sub-pixel skip fusion method to perform both channel enhancement and upsampling. Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction. Then we propose a sub-pixel context enhancement module for extracting more feature representations, which is superior to other context methods due to the utilization of rich channel information by sub-pixel convolution. Furthermore, a channel attention guided module is introduced to optimize the final integrated features on each level, which alleviates the aliasing effect only with a few computational burdens. Our experiments show that CE-FPN achieves competitive performance compared to state-of-the-art FPN-based detectors on MS COCO benchmark.

CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions

Natural Language (NL) descriptions can be the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle-related events. In this paper, we extend the widely adopted CityFlow Benchmark with natural language descriptions for vehicle targets and introduce the CityFlow-NL Benchmark. The CityFlow-NL contains more than 5,000 unique and precise NL descriptions of vehicle targets, making it the largest-scale tracking with NL descriptions dataset to our knowledge. Moreover, the dataset facilitates research at the intersection of multi-object tracking, retrieval by NL descriptions, and temporal localization of events.

CityFlow-NL: Tracking and Retrieval of Vehicles at City Scaleby Natural Language Descriptions

Natural Language (NL) descriptions can be the most convenient or the only way to interact with systems built to understand and detect city scale traffic patterns and vehicle-related events. In this paper, we extend the widely adopted CityFlow Benchmark with natural language descriptions for vehicle targets and introduce the CityFlow-NL Benchmark. The CityFlow-NL contains more than 5,000 unique and precise NL descriptions of vehicle targets, making it the largest-scale tracking with NL descriptions dataset to our knowledge. Moreover, the dataset facilitates research at the intersection of multi-object tracking, retrieval by NL descriptions, and temporal localization of events.

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction

Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration. To address this issue, we study the variance reduction for noisy energy estimators, which promotes much more effective swaps. Theoretically, we provide a non-asymptotic analysis on the exponential acceleration for the underlying continuous-time Markov jump process; moreover, we consider a generalized Girsanov theorem which includes the change of Poisson measure to overcome the crude discretization based on the Gr\"{o}wall's inequality and yields a much tighter error in the 2-Wasserstein ($\mathcal{W}_2$) distance. Numerically, we conduct extensive experiments and obtain the state-of-the-art results in optimization and uncertainty estimates for synthetic experiments and image data.

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC

Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms. However, such a method requires the evaluation of the energy function based on the full dataset and is not scalable to big data. The na\"ive implementation of reMC in mini-batch settings introduces large biases, which cannot be directly extended to the stochastic gradient MCMC (SGMCMC), the standard sampling method for simulating from deep neural networks (DNNs). In this paper, we propose an adaptive replica exchange SGMCMC (reSGMCMC) to automatically correct the bias and study the corresponding properties. The analysis implies an acceleration-accuracy trade-off in the numerical discretization of a Markov jump process in a stochastic environment. Empirically, we test the algorithm through extensive experiments on various setups and obtain the state-of-the-art results on CIFAR10, CIFAR100, and SVHN in both supervised learning and semi-supervised learning tasks.

SiamSNN: Spike-based Siamese Network for Energy-Efficient and Real-time Object Tracking

Although deep neural networks (DNNs) have achieved fantastic success in various scenarios, it's difficult to employ DNNs on many systems with limited resources due to their high energy consumption. It's well known that spiking neural networks (SNNs) are attracting more attention due to the capability of energy-efficient computing. Recently many works focus on converting DNNs into SNNs with little accuracy degradation in image classification on MNIST, CIFAR-10/100. However, few studies on shortening latency, and spike-based modules of more challenging tasks on complex datasets. In this paper, we focus on the similarity matching method of deep spike features and present a first spike-based Siamese network for object tracking called SiamSNN. Specifically, we propose a hybrid spiking similarity matching method with membrane potential and time step to evaluate the response map between exemplar and candidate images, with the same function as correlation layer in SiamFC. Then we present a coding scheme for utilizing temporal information of spike trains, and implement it in output spiking layers to improve the performance and shorten the latency. Our experiments show that SiamSNN achieves short latency and low precision loss of the original SiamFC on the tracking datasets OTB-2013, OTB-2015 and VOT2016. Moreover, SiamSNN achieves real-time (50 FPS) and extremely low energy consumption on TrueNorth.

Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes

In the past decade, deep learning based visual object detection has received a significant amount of attention, but cases when heavy intra-class occlusions occur are not studied thoroughly. In this work, we propose a novel Non-MaximumSuppression (NMS) algorithm that dramatically improves the detection recall while maintaining high precision in scenes with heavy occlusions. Our NMS algorithm is derived from a novel embedding mechanism, in which the semantic and geometric features of the detected boxes are jointly exploited. The embedding makes it possible to determine whether two heavily-overlapping boxes belong to the same object in the physical world. Our approach is particularly useful for car detection and pedestrian detection in urban scenes where occlusions tend to happen. We validate our approach on two widely-adopted datasets, KITTI and CityPersons, and achieve state-of-the-art performance.

Robust Visual Object Tracking with Natural Language Region Proposal Network

Tracking with natural-language (NL) specification is a powerful new paradigm to yield trackers that initialize without a manually-specified bounding box, stay on target in spite of occlusions, and auto-recover when diverged. These advantages stem in part from visual appearance and NL having distinct and complementary invariance properties. However, realizing these advantages is technically challenging: the two modalities have incompatible representations. In this paper, we present the first practical and competitive solution to the challenge of tracking with NL specification. Our first novelty is an NL region proposal network (NL-RPN) that transforms an NL description into a convolutional kernel and shares the search branch with siamese trackers; the combined network can be trained end-to-end. Secondly, we propose a novel formulation to represent the history of past visual exemplars and use those exemplars to automatically reset the tracker together with our NL-RPN. Empirical results over tracking benchmarks with NL annotations demonstrate the effectiveness of our approach.