STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning

Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms. An important factor is that they typically involve cooperative learning across multi-resolutions, which could be suboptimal for learning the most discriminative features from each scale. In this paper, we propose a novel method termed STEERER (\textbf{S}elec\textbf{T}iv\textbf{E} inh\textbf{ER}itance l\textbf{E}a\textbf{R}ning) that addresses the issue of scale variations in object counting. STEERER selects the most suitable scale for patch objects to boost feature extraction and only inherits discriminative features from lower to higher resolution progressively. The main insights of STEERER are a dedicated Feature Selection and Inheritance Adaptor (FSIA), which selectively forwards scale-customized features at each scale, and a Masked Selection and Inheritance Loss (MSIL) that helps to achieve high-quality density maps across all scales. Our experimental results on nine datasets with counting and localization tasks demonstrate the unprecedented scale generalization ability of STEERER. Code is available at \url{https://github.com/taohan10200/STEERER}.Comment: Accepted by ICCV2023, 9 page

Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches

Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for MoCo v3) to achieve promising results, which is unacceptable for the general academic community and hinders the development of this topic. This work revisits the momentum-based contrastive learning frameworks and identifies the inefficiency in which two augmented views generate only one positive pair. We propose Fast-MoCo - a novel framework that utilizes combinatorial patches to construct multiple positive pairs from two augmented views, which provides abundant supervision signals that bring significant acceleration with neglectable extra computational cost. Fast-MoCo trained with 100 epochs achieves 73.5% linear evaluation accuracy, similar to MoCo v3 (ResNet-50 backbone) trained with 800 epochs. Extra training (200 epochs) further improves the result to 75.1%, which is on par with state-of-the-art methods. Experiments on several downstream tasks also confirm the effectiveness of Fast-MoCo.Comment: Accepted for publication at the 2022 European Conference on Computer Vision (ECCV 2022

Towards Frame Rate Agnostic Multi-Object Tracking

Multi-Object Tracking (MOT) is one of the most fundamental computer vision tasks which contributes to a variety of video analysis applications. Despite the recent promising progress, current MOT research is still limited to a fixed sampling frame rate of the input stream. In fact, we empirically find that the accuracy of all recent state-of-the-art trackers drops dramatically when the input frame rate changes. For a more intelligent tracking solution, we shift the attention of our research work to the problem of Frame Rate Agnostic MOT (FraMOT). In this paper, we propose a Frame Rate Agnostic MOT framework with Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations in FraMOT. Besides, the association gap between training and inference is enlarged in FraMOT because those post-processing steps not included in training make a larger difference in lower frame rate scenarios. To address it, we propose Periodic Training Scheme (PTS) to reflect all post-processing steps in training via tracking pattern matching and fusion. Along with the proposed approaches, we make the first attempt to establish an evaluation method for this new task of FraMOT in two different modes, i.e., known frame rate and unknown frame rate, aiming to handle a more complex situation. The quantitative experiments on the challenging MOT datasets (FraMOT version) have clearly demonstrated that the proposed approaches can handle different frame rates better and thus improve the robustness against complicated scenarios.Comment: 21 pages; Author versio

Evaluation System of High-quality Development of Cities in the Yangtze River Economic Belt under the New Development Pattern

In view of the high-quality development of cities in the Yangtze River Economic Belt, and in combination with the characteristics of the new development pattern, this paper constructs the evaluation index system of high-quality development of cities in the Yangtze River Economic Belt from five aspects which contain innovative development, coordinated development, green development, open development, and shared development. Firstly, this paper constructs the evaluation model of the high-quality development of the cities in the Yangtze River Economic Belt. Secondly, the paper uses the analytic hierarchy process to determine the weight of the indicators. Thirdly, the paper uses the fuzzy comprehensive evaluation method to establish the evaluation set of the indicators. Finally, it concludes that the high-quality development level of the cities in the Yangtze River Economic Belt is at the middle level

Short O-O separation in layered oxide Na0.67CoO2 enables an ultrafast oxygen evolution reaction.

The layered oxide Na0.67CoO2 with Na+ occupying trigonal prismatic sites between CoO2 layers exhibits a remarkably high room temperature oxygen evolution reaction (OER) activity in alkaline solution. The high activity is attributed to an unusually short O-O separation that favors formation of peroxide ions by O--O- interactions followed by O2 evolution in preference to the conventional route through surface O-OH- species. The dependence of the onset potential on the pH of the alkaline solution was found to be consistent with the loss of H+ ions from the surface oxygen to provide surface O- that may either be attacked by solution OH- or react with another O-; a short O-O separation favors the latter route. The role of a strong hybridization of the O-2p and low-spin CoIII/CoIV π-bonding d states is also important; the OER on other CoIII/CoIV oxides is compared with that on Na0.67CoO2 as well as that on IrO2

Memory-aware embedded control systems design

Control applications are often implemented on highly cost-sensitive and resource-constrained embedded platforms, such as microcontrollers with a small on-chip memory. Typically, control algorithms are designed using model-based approaches, where the details of the implementation platform are completely ignored. As a result, optimizations that integrate platform-level characteristics into the control algorithms design are largely missing. With the emergence of cyber-physical systems (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms, leading to work on networked control systems and computation-aware control algorithms design. However, there has so far been no work on integrating the characteristics of a memory architecture into the design of control algorithms. In this paper we, for the first time, show that accounting for the impact of on-chip memory (or cache) reuse on the performance of control applications motivates new techniques for control algorithms design. This leads to significant improvement in quality of control for given resource availability, or more efficient implementations of embedded control applications. We believe that this paper opens up a variety of possibilities for memory-related optimizations of embedded control systems, that will be pursued by researchers working on computer-aided design for CPS

Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images

Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish trust in photographs. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content. Our study benchmarks both human capability and cutting-edge fake image detection AI algorithms, using a newly collected large-scale fake image dataset Fake2M. In our human perception evaluation, titled HPBench, we discovered that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of 38.7%. Along with this, we conduct the model capability of AI-Generated images detection evaluation MPBench and the top-performing model from MPBench achieves a 13% failure rate under the same setting used in the human evaluation. We hope that our study can raise awareness of the potential risks of AI-generated images and facilitate further research to prevent the spread of false information. More information can refer to https://github.com/Inf-imagine/Sentry

Terahertz Sensor via Ultralow-Loss Dispersion-Flattened Polymer Optical Fiber: Design and Analysis

A novel cyclic olefin copolymer (COC)-based polymer optical fiber (POF) with a rectangular porous core is designed for terahertz (THz) sensing by the finite element method. The numerical simulations showed an ultrahigh relative sensitivity of 89.73% of the x-polarization mode at a frequency of 1.2 THz and under optimum design conditions. In addition to this, they showed an ultralow confinement loss of 2.18 × 10−12 cm−1, a high birefringence of 1.91 × 10−3, a numerical aperture of 0.33, and an effective mode area of 1.65 × 105 μm2 was obtained for optimum design conditions. Moreover, the range dispersion variation was within 0.7 ± 0.41 ps/THz/cm, with the frequency range of 1.0–1.4 THz. Compared with the traditional sensor, the late-model sensor will have application value in THz sensing and communication