Miracle-dz/UniFAR-FTUs huggingface dataset:
LACFormer: Toward accurate and efficient polyp segmentation

Polyp segmentation is an essential task in medical image analysis for early detection of colorectal cancer. Deep learning models, particularly encoder-decoder architectures, have been successful in polyp segmentation. However, these models often struggle to capture long-range dependencies and exhibit limited performance on small polyps. In this paper, we propose LACFormer, a novel hierarchical Transformer-CNN model incorporating the Laplacian pyramid for polyp segmentation. The proposed model combines the strengths of Transformers and CNNs along with Laplacian images to overcome the limitations of previous models. Specifically, the hierarchical Transformer backbone captures long-range dependencies and hierarchically processes the features to generate multi-scale representations. These representations are then fused with a novel CNN de-coder, which enhances feature representations and refines the segmentation masks. Besides, many novel modules for effective polyp segmentation are also proposed. We evaluated our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that LACFormer outperforms state-of-the-art models, achieving a Dice similarity coefficient (DSC) of 0.927 and a mean intersection-over-union (mIoU) of 0.878 on CVC-ClinicDB, a DSC of 0.831 and mIoU of 0.753 on CVC-ColonDB and a DSC of 0.824 and mIoU of 0.753 on ETIS-Larib. Code is available at: https://github.com/sun-asterisk-research/LACFormer.
One-stage Progressive Dichotomous Segmentation

Dichotomous segmentation is a challenging task that involves recognizing foreground objects in high-resolution images with varying characteristics. Existing methods often miss important details of the object or require a long processing time due to multi-stage process. In this paper, we propose a one-stage effective model that can distinguish objects in dichotomous segmentation with low computation cost. Unlike most methods that use two separate branches to first obtain coarse results from low-resolution images and then refine them with the high-resolution information, our method can directly process high-resolution inputs with simple operations. We introduce convolutional attentions into the feature extractor to effectively capture multi-scale features. These features are then used to generate high-quality results with a specifically designed progressive decoder. The experimental results demonstrate that our method achieves superior performance on the DIS5K dichotomous segmentation dataset with fewer model parameters and computational operations.
Short-term Load Forecasting Method of Power Distribution Station Area Integrating Multiple Temporal Characteristics

In order to solve the problem of short term power load forecasting and improve the accuracy of load forecasting in distribution station area, a short term power load forecasting model integrating multiple temporal series characteristics is proposed in this paper. Firstly, we solve the problems of missing and outliers in acquired load and weather data through data preprocessing. Then, we consider the historical load, weather, working day/holiday and other factors, then explore the long-short term, periodicity and particularity of the load and weather information. After that, we establish the short-term power load prediction feature model of the platform area. In addition, we use the deep residual network as the basic structure to eliminate the overfitting problem caused by the deep network. Experiments show that the proposed method has smaller prediction error compared with the existing methods.
Analysis of heavy-rain-producing elevated thunderstorms in the MO-KS-OK region of the United States

Presented is a composite analysis of elevated thunderstorms that produced heavy rainfall in the Springfield, Kansas City, Topeka, Wichita, and Tulsa National Weather Service County Warning Areas (CWAs) during the warm season (April through September) between 1979 and 2012. These CWAs encompass the region which Colman (1990) found experiences the most elevated thunderstorms in the United States. The events composited produced over two inches of rain in 24 hours, and composites were generated for the time just prior to the heaviest rainfall, six hours prior, and twelve hours prior. The results concluded that in all five CWAs, heavyrainfall-producing elevated thunderstorms resulted from the cooperation of the low-level jet with the right-entrance region of the upper-level jet. Convection and heavy rainfall commences just as the low-level jet ramps up from the south-southwest in the region of maximized convergence above the stable boundary layer. Ample moisture is present even 12 hours prior to the event, with the low-level jet advecting in even more moisture at the beginning of the event. K-index values show well the destabilization of the layer above the low-level stable layer, with values increasing from 30 to 35 just prior to the event at the location of maximum precipitation. The location of the nose of the low-level jet, the K-index maximum, and the 250-hPA divergence maximum together give a very good indication of where the placement of the heaviest precipitation is located.
Fusion of Image and Point Cloud for Accurate Obstacle Detection in Autonomous Driving

Obstacle detection is of great significance for autonomous driving. Accurately identifying obstacles and transmitting obstacle information to the underlying planning and control system is essential for effective and safe autonomous driving. Obstacle detection is commonly performed using sensors such as LiDAR and cameras. LiDAR has poor detection performance for small objects, while cameras excel at recognizing various objects but cannot directly obtain object position information. A good solution for obstacle detection is to combine cameras and LiDAR. In this approach, obstacle detection involves first detecting bounding boxes of obstacles from the image data obtained by the camera, and then matching these bounding boxes with point cloud data obtained from the LiDAR to obtain precise information about the obstacles. Obtaining accurate detection bounding boxes is an important challenge in this approach. Therefore, this paper designs experiments to compare the detection performance of different object recognition algorithms in terms of bounding box detection. Comparative experiments have demonstrated that the YOLOv8 algorithm exhibits excellent performance in image object detection. When combined with point cloud data, it achieves precise detection of obstacles in autonomous driving, yielding accurate results.
A low-cost parallel K-means VQ algorithm using cluster computing

In this paper we propose a parallel approach for the K-meansVector Quantization (VQ) algorithm used in a two-stageHidden Markov Model (HMM)-based system forrecognizing handwritten numeral strings. With thisparallel algorithm, based on the master/slave paradigm,we overcome two drawbacks of the sequential version: a)the time taken to create the codebook; and b) the amountof memory necessary to work with large trainingdatabases. Distributing the training samples over theslaves' local disks reduces the overhead associated withthe communication process. In addition, modelspredicting computation and communication time havebeen developed. These models are useful to predict theoptimal number of slaves taking into account the numberof training samples and codebook size.
Robust and Efficient Edge-guided Pose Estimation with Resolution-conditioned NeRF

Monocular 6D pose estimation attempts to obtain the 3D location and rotation of an object from a single input image. Deep Learning based methods have lead to large improvements in this area, but still require highly realistic 3D CAD models or manual data labeling. Neural Radiance Fields (NeRF) have made significant progress in solving the inverse problem of realistically rendering an object from a novel pose without these requirements. Nevertheless, current methods that try to harness them to extract the object’s pose from a given image using an analysis-by-synthesis approach lack robustness and speed, and require hours of pretraining on the object of interest. We propose a novel pose estimation pipeline that makes NeRF-based analysis-by-synthesis reliable and fast. Our proposal improves the quality of the optimization by 1) changing the representation of the pose to a decoupled and continuous parameterization, 2) increasing the model’s robustness to changes in scale by means of conditioning it on the used resolution, and 3) developing an edge-based sampling strategy that focuses on shooting rays near image regions with a strong learning signal. These improvements, along with our backbone choice, allow us to estimate the pose with more than 5% higher recall and more than 4 times faster than prior work, while reducing the pretraining time from hours to minutes.
Frequency-consistent Optimization for Image Enhancement Networks

Image enhancement aims at enhancing the overall contrast (low frequency) while reconstructing details (high frequency). Existing studies typically achieve these two objectives with a heuristically constructed complex architecture ( i.e. , two-stage or two-branch). In contrast, we attempt to perform the image enhancement task within a single-stage and single-branch network. However, directly employing a single plain network to optimize the two objectives simultaneously will lead to an optimization conflict between contrast enhancement and texture restoration, resulting in suboptimal performances. To alleviate this problem, we construct a frequency-independent feature space for maintaining optimization consistency. Specifically, we propose a Frequency Decorrelation and Integration (FDI) module with two core insights: 1) formulating a frequency-independent space via decorrelation normalization to bridge the frequency discrimination; 2) integrating the initial frequency-dependent features with a channel shuffle operation for information complement and reducing the sensitivity to frequency during optimization. Therefore, these two designs encourage networks to learn along the optimization direction of frequency consistency. In addition, the proposed FDI is a plug-and-play module that can be incorporated into the existing methods with negligible parameters. Extensive experiments on various image enhancement benchmarks demonstrate consistent performance gains by utilizing our proposed module.
On the Complexity and Accuracy of Geographic

Geographic profilers have access to a repertoire of strategies for predicting a serial offender's home location. These strategies range in complexity—some in volve more calculations to implement than others—and the assumption often made is that more complex strategies will outperform simpler strategies. In the present study, we tested the relationship between the complexity and accuracy of 11 strategies. Data were crime site and home locations of 16 UK residential burglars who had committed 10 or more crimes each. The results indicated that strategy complexity was not positively related to accuracy. This was also found to be the case across tasks that ranged in complexity (where complexity was determined by the number of crimes used to make a prediction). Implications for police' policies and procedures, as well as our understanding of human decision making, are discussed.
Vision Transformers are Inherently Saliency Learners

Training a Convolutional neural network’s (CNNs) auto-encoder has been the defacto approach for visual attention modelling. Recently, (Vision) Transformer models (ViT) achieved excellent performance on various computer vision tasks. In this context, the self-attention mechanism plays a crucial role enabling early aggregation of global information, and ViT residual connections strongly propagate features from lower to higher layers. This raises two important questions: are Vision Transformers inherently learning saliency maps? Are the self-attention maps focusing on the salient regions of the input image? Analyzing the self-attention maps of a pretrained ViTs on saliency prediction datasets, we find that smoothing the internal attention maps with a small number of convolutional filters can achieve reasonable saliency maps with acceptable metric scores. We explore how this phenomenon arises, finding that self-attention promotes early aggregation of global information, then in higher layers, it associates highly attended features, compares their dependencies, and makes analogies over the recurring patterns. This suggests that ViTs first perform feature search, followed by conjunction search combining multiple features sharing higher mutual information. We study the analogies between the self-attention maps and the human generated saliency maps, and conclude with a discussion on the relationship to human visual attention such as feature integration theory.
On-Site Adaptation for Monocular Depth Estimation with a Static Camera

We introduce a novel technique for easing the deployment of an off-the-shelf monocular depth estimation network in unseen environments. Specifically, we target a very diffused setting with a fixed camera mounted higher over the ground to monitor an environment and highlight the limitations of state-of-the-art monocular networks deployed in such a setup. Purposely, we develop an on-site adaptation technique capable of 1) improving the accuracy of estimated depth maps in the presence of moving subjects, such as pedestrians, cars, and others; 2) refining the overall structure of the predicted depth map, to make it more consistent with the real 3D structure of the scene; 3) recovering absolute metric depth, usually lost by state-of-the-art solutions. Experiments on synthetic and real datasets confirm the effectiveness of our proposal.
RGB and LUT based Cross Attention Network for Image Enhancement

Image enhancement aims to improve the quality of images by adjusting their color and is widely used in professional digital photography. Deep learning-based 3 D imensional L ook U p-T able (3D LUT) of RGB color transformation has achieved promising performance in terms of speed and precision. However, the focus has mainly been on building an adaptive enhancer by only learning the global color adjusting weights from the image, which ignores the signiﬁcant relationship between the intrinsic semantic information of the image and LUT that is relevant to photographers. In this paper, we propose the C ross A ttention N etwork (CANet), a new framework that formulates image enhancement as a parallel learning process based on the image and LUT features. To better learn the adjustment weights for both global color and intrinsic semantics, we propose a cross attention architecture that connects low-level (color, edge and outline) and high-level (semantic) features of the image and color transform LUT features to generate appropriate adjustment weights. Meanwhile, we employ a L UT-A ware M odule (LAM) to construct the channels and spatial attention for reﬁning the LUT features. Since these modules have a more powerful representational capacity, they can better capture the intrinsic relationship between image semantics and LUT features. The extensive evaluations on standard benchmarks, including FiveK and HDR datasets, show that CANet achieves better performance compared to state-of-the-art methods.
Embedding Human Knowledge into Spatio-Temproal Attention Branch Network in Video Recognition via Temporal attention

When recognizing objects or motions, humans can accurately judge the necessary areas for recognition. In contrast, recognition using a deep learning model is based on its training data and may fail to focus on the correct regions. In image recognition, it has been shown that visualizing the basis of decisions and embedding human knowledge into deep neural networks are effective in addressing this issue. However, in video recognition, there is no visualization method enabling us to embed human knowledge. We propose the spatio-temporal attention branch network (ST-ABN) for video recognition, which provides visual explanations for both spatial and temporal attentions. One of the features of the ST-ABN is that its attention output can be modified on the basis of human knowledge and used for recognition. However, since a video consists of a large number of frame images, modifying spatial attentions similar to image recognition is costly. Therefore, we manually modify temporal attentions to embed human knowledge into the ST-ABN. Experimental results with Something-Something v.2 indicate that the ST-ABN provides visual explanation for both spatial and temporal information and improves recognition performance. The results also indicate the effectiveness of embedding human knowledge into the ST-ABN and the positive changes in spatial attentions by modifying temporal attentions.
Domain-Sum Feature Transformation For Multi-Target Domain Adaptation

Domain adaptation effectively transfers a learner from a source domain to a target domain. Recent deep methods are based on detailed comparison between a pair of source and target domains, which makes it less applicable to multiple domains. In this paper, we address the domain adaptation on the basis of subspace which provides more robust metric. We analyze the subspace methods in domain adaptation to theoretically derive a subspace-based feature transformation in an efﬁcient form of simple summation. It intrinsically contributes to closing a gap between source and target subspaces in an end-to-end deep framework. Besides, due to the robust representation of subspace and the simple transformation, the proposed method naturally deals with multiple domains both for source and target in contrast to previous approaches. Multi-target domain adaptation especially provides efﬁcient inference to process multiple target domains by only a single model. In the experiments on visual domain adaptation tasks, the proposed method exhibits favorable performance in a scenario of the multi-target domains.
Cross-Modal Attention for Accurate Pedestrian Trajectory Prediction

Accurately predicting human behavior is essential for a variety of applications, including self-driving cars, surveillance systems, and social robots. However, predicting human movement is challenging due to the complexity of physical environments and social interactions. Most studies focus on static environmental information, while ignoring the dynamic visual information available in the scene. To address this issue, we propose a novel approach called Cross-Modal Attention Trajectory Prediction (CMATP) able to predict human paths based on observed trajectory and dynamic scene context. Our approach uses a bimodal transformer network to capture complex spatio-temporal interactions and incorporates both pedestrian trajectory data and contextual information. Our approach achieves state-of-the-art performance on three real-world pedestrian prediction datasets, making it a promising solution for improving the safety and reliability of pedestrian detection and tracking systems. The code to reproduce our results is available at this link.
Lightweight Self-Supervised Depth Estimation with few-beams LiDAR Data

This paper proposes a lightweight yet effective self-supervised depth completion network trained on monocular videos and sparse raw LiDAR measurements only. Specifically, we utilize a multi-stage network architecture, which depends on cheap CNN layers. We introduce a novel guided sparse convolution operator combining sparse and dense data to extract depth features. To mitigate the impact of outliers commonly present in the sparse raw LiDAR data, we adopt a distance-dependent outlier mask that incorporates an elastic threshold mechanism to selectively discard such points. Our experimental re-sults on the KITTI dataset show the favorable trade-off between accuracy and efficiency achieved by our model, reaching state-of-the-art performance on self-supervised depth estimation from few-beams LiDAR (4-beams), depth completion (64-beams) and a few hundred depth points, using a fraction of the parameters. Our code will be available on https://github.com/franky-ciomp/GSCNN/ .
Instance Mask Growing on Leaf

Contour-based instance segmentation methods represent masks through a series of points. However, the point number is fixed once the model is trained, which limits the model’s flexibility in dealing with various instances. We follow this issue and present an idea to predict an appropriate number of points dynamically according to instance shapes. Concretely, we observe that the leaf locates coarse margins via major veins and grows minor veins to refine twisty parts, which helps cover any masks accurately. Mean-while, major and minor veins share the same growth mode, which makes it possible to generate minor veins dynamically according to the trained major vein mode. Considering the superiorities above, we propose VeinMask to formulate the instance segmentation problem as the simulation of the vein growth process and to predict the major and minor veins in polar coordinates for instance segmenting. Besides, centroidness is introduced for instance segmentation tasks to help suppress low-quality instances. Furthermore, a surroundings cross-correlation sensitive (SCCS) module is designed to enhance the feature expression by utilizing the surroundings of each pixel. Additionally, a Residual IoU (RIoU) loss is formulated to supervise the regression tasks of major and minor veins effectively. Experiments demonstrate the effectiveness of VeinMask. Particularly, our method outperforms existing one-stage contour-based methods on the COCO dataset with almost half the trained point number. Code is available at: https://github.com/omtcyang/veinmask .
Adaptive Adversarial Norm Space for Efficient Adversarial Training

Adversarial training draws increasing attention as it can improve the robustness of deep neural networks against adversarial examples. Recent research proposed to adaptively adjust the adversarial strategy for a better learning process. However, those approaches rely on cumbersome computations for getting the optimal adversarial strategy. This paper offers a novel perspective on adversarial strategies by examining the adversarial examples’ norm space. We show that cyclically altering the adversarial norm space can significantly enhance the network’s robustness. Based on the observations, we propose a simple yet effective Entropy-Guided Cyclical Adversarial Strategy (ECAS) to explicitly adjust the norm space of the adversarial examples, forming an elastic-perturbation mechanism in the adversarial training framework that adaptively perturbs models based on entropy. Extensive experiments demonstrate that our proposed method can achieve promising performances and substantially reduce computational time compared to state-of-the-art methods. Moreover, we also show that ECAS can be directly plugged into existing adversarial training methods to further boost performances. The implementation of ECAS is at https://github.com/huizhg/ECAS .
SlackedFace: Learning a Slacked Margin for Low-Resolution Face Recognition

Low-resolution (LR) face recognition poses a significant challenge in embedding learning due to the severe loss of identity information. Recent softmax losses introduce a non-static margin that assigns greater importance or a larger margin to recognizable examples based on embedding norm as a measure of face recognizability. In this paper, we argue that face recognizability is more than just the embedding norm, as it does not capture the identity-level details that are important to embedding learning. We propose SlackedFace to induce a relaxed margin aligned with face recognizability and the model’s confidence based on both embedding norm and embedding proximity for empowered embedding learning. We also put forward fast-hill climbing as an early calibration stage between pre-trained and randomly initialized modules. We show that SlackedFace outperforms the current best models on realistic LR face datasets when tested in practical open-set evaluation scenarios.