Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads

With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small & medium sized pedestrians and achieves state-of-the-art results in these challenging datasets.

Detection of Pavement Cracks by Deep Learning Models of Transformer and UNet

Fracture is one of the main failure modes of engineering structures such as buildings and roads. Effective detection of surface cracks is significant for damage evaluation and structure maintenance. In recent years, the emergence and development of deep learning techniques have shown great potential to facilitate surface crack detection. Currently, most reported tasks were performed by a convolutional neural network (CNN), while the limitation of CNN may be improved by the transformer architecture introduced recently. In this study, we investigated nine promising models to evaluate their performance in pavement surface crack detection by model accuracy, computational complexity, and model stability. We created 711 images of 224 by 224 pixels with crack labels, selected an optimal loss function, compared the evaluation metrics of the validation dataset and test dataset, analyzed the data details, and checked the segmentation outcomes of each model. We find that transformer-based models generally are easier to converge during the training process and have higher accuracy, but usually exhibit more memory consumption and low processing efficiency. Among nine models, SwinUNet outperforms the other two transformers and shows the highest accuracy among nine models. The results should shed light on surface crack detection by various deep-learning models and provide a guideline for future applications in this field.

Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning

Asymmetrical multiplayer (AMP) game is a popular game genre which involves multiple types of agents competing or collaborating with each other in the game. It is difficult to train powerful agents that can defeat top human players in AMP games by typical self-play training method because of unbalancing characteristics in their asymmetrical environments. We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game. We designed adaptive data adjustment (ADA) and environment randomization (ER) to optimize the AET process. We tested our method in a complex AMP game named Tom \& Jerry, and our AIs trained without using any human data can achieve a win rate of 98.5% against top human players over 65 matches. The ablation experiments indicated that the proposed modules are beneficial to the framework.

Personalized Federated Learning with Local Attention

Federated Learning (FL) aims to learn a single global model that enables the central server to help the model training in local clients without accessing their local data. The key challenge of FL is the heterogeneity of local data in different clients, such as heterogeneous label distribution and feature shift, which could lead to significant performance degradation of the learned models. Although many studies have been proposed to address the heterogeneous label distribution problem, few studies attempt to explore the feature shift issue. To address this issue, we propose a simple yet effective algorithm, namely \textbf{p}ersonalized \textbf{Fed}erated learning with \textbf{L}ocal \textbf{A}ttention (pFedLA), by incorporating the attention mechanism into personalized models of clients while keeping the attention blocks client-specific. Specifically, two modules are proposed in pFedLA, i.e., the personalized single attention module and the personalized hybrid attention module. In addition, the proposed pFedLA method is quite flexible and general as it can be incorporated into any FL method to improve their performance without introducing additional communication costs. Extensive experiments demonstrate that the proposed pFedLA method can boost the performance of state-of-the-art FL methods on different tasks such as image classification and object detection tasks.

SPColor: Semantic Prior Guided Exemplar-based Image Colorization

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching is easy to get mismatch. We summarize the difficulties in two aspects: (1) When the reference image only contains a part of objects related to target image, improper correspondence will be established in unrelated regions. (2) It is prone to get mismatch in regions where the shape or texture of the object is easily confused. To overcome these issues, we propose SPColor, a semantic prior guided exemplar-based image colorization framework. Different from previous methods, SPColor first coarsely classifies pixels of the reference and target images to several pseudo-classes under the guidance of semantic prior, then the correspondences are only established locally between the pixels in the same class via the newly designed semantic prior guided correspondence network. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. Besides, to better reserve the color from reference, a similarity masked perceptual loss is designed. Noting that the carefully designed SPColor utilizes the semantic prior provided by an unsupervised segmentation model, which is free for additional manual semantic annotations. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset.

Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference

We propose Conditional Adapter (CoDA), a parameter-efficient transfer learning method that also improves inference efficiency. CoDA generalizes beyond standard adapter approaches to enable a new way of balancing speed and accuracy using conditional computation. Starting with an existing dense pretrained model, CoDA adds sparse activation together with a small number of new parameters and a light-weight training phase. Our experiments demonstrate that the CoDA approach provides an unexpectedly efficient way to transfer knowledge. Across a variety of language, vision, and speech tasks, CoDA achieves a 2x to 8x inference speed-up compared to the state-of-the-art Adapter approach with moderate to no accuracy loss and the same parameter efficiency.

A Fast and Lightweight Network for Low-Light Image Enhancement

Low-light images often suffer from severe noise, low brightness, low contrast, and color deviation. While several low-light image enhancement methods have been proposed, there remains a lack of efficient methods that can simultaneously solve all of these problems. In this paper, we introduce FLW-Net, a Fast and LightWeight Network for low-light image enhancement that significantly improves processing speed and overall effect. To achieve efficient low-light image enhancement, we recognize the challenges of the lack of an absolute reference and the need for a large receptive field to obtain global contrast. Therefore, we propose an efficient global feature information extraction component and design loss functions based on relative information to overcome these challenges. Finally, we conduct comparative experiments to demonstrate the effectiveness of the proposed method, and the results confirm that FLW-Net can significantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect. Code is available at https://github.com/hitzhangyu/FLW-Net

Safe Explicable Robot Planning

Human expectations stem from their knowledge of the others and the world. Where human-robot interaction is concerned, such knowledge about the robot may be inconsistent with the ground truth, resulting in the robot not meeting its expectations. Explicable planning was previously introduced as a novel planning approach to reconciling human expectations and the optimal robot behavior for more interpretable robot decision-making. One critical issue that remains unaddressed is safety during explicable decision-making which can lead to explicable behaviors that are unsafe. We propose Safe Explicable Planning (SEP), which extends explicable planning to support the specification of a safety bound. The objective of SEP is to find a policy that generates a behavior close to human expectations while satisfying the safety constraints introduced by the bound, which is a special case of multi-objective optimization where the solution to SEP lies on the Pareto frontier. Under such a formulation, we propose a novel and efficient method that returns the safe explicable policy and an approximate solution. In addition, we provide theoretical proof for the optimality of the exact solution under the designer-specified bound. Our evaluation results confirm the applicability and efficacy of our method for safe explicable planning.