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In this paper, we investigate cross-lingual learning (CLL) for multilingual scene text recognition (STR). CLL transfers knowledge from one language to another. We aim to find the condition that exploits knowledge from high-resource languages for improving performance in low-resource languages. To do so, we first examine if two general insights about CLL discussed in previous works are applied to multilingual STR: (1) Joint learning with high- and low-resource languages may reduce performance on low-resource languages, and (2) CLL works best between typologically similar languages.

We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor. Tensor descriptors require a robust similarity measure due to low numbers of aggregated vectors and the burstiness phenomenon, when a given feature appears more/less frequently than statistically expected. The Heat Diffusion Process (HDP) on a graph Laplacian is closely related to the Eigenvalue Power Normalization (EPN) of the covariance/auto-correlation matrix, whose inverse forms a loopy graph Laplacian.

Haze causes information loss and quality degradation in remote sensing images. Unsupervised learning-based dehazing methods aim to reduce reliance on paired hazy images and their labels. However, complex mapping relationships often increase the difficulty in network convergence, resulting in color distortion and loss of texture details in remote sensing images. To address these issues, we propose an unsupervised haze removal method based on saliency-guided transmission refinement for remote sensing images.

Human-Object Interaction (HOI) detection is a crucial task that involves localizing interactive human-object pairs and identifying the actions being performed. Most existing HOI detectors are supervised in nature and lack the ability of zero-shot discovery of unseen interactions. Recently, transformer-based methods have superseded the traditional CNN detectors by aggregating image-wide context but still suffer from the long-tail distribution problem in HOI. In this work, our primary focus is improving HOI detection in images, particularly in zero-shot scenarios.

Various research studies indicate that action recognition performance highly depends on the types of motions being extracted and how accurate the human actions are represented. In this paper, we investigate different optical flow, and features extracted from these optical flow that capturing both short-term and long-term motion dynamics. We perform power normalization on the magnitude component of optical flow for flow dynamics correction to boost subtle or dampen sudden motions.

Occlusion presents a significant challenge in human pose estimation. The challenges posed by occlusion can be attributed to the following factors: 1) Data: The collection and annotation of occluded human pose samples are relatively challenging. 2) Feature: Occlusion can cause feature confusion due to the high similarity between the target person and interfering individuals. 3) Inference: Robust inference becomes challenging due to the loss of complete body structural information.

Due to the rich details of residential areas and the characteristics of remote sensing image sharpness vulnerable to haze, it will not only consume a lot of labor costs but also be very difficult to produce a large-scale dataset with strong labels. Therefore, the limited-sample dataset has become a hotspot in recent years. To address this issue, we proposed a semantic segmentation method for residential areas by phase learning.

The technique of semantic segmentation (SS) holds significant importance in the domain of remote sensing image (RSI) processing. The current research primarily encompasses two problems: 1) RSIs are easily affected by clouds and haze; 2) SS based on strong annotation requires vast human and time costs. In this paper, we propose a weakly supervised semantic segmentation (WSSS) method for hazy RSIs based on saliency-aware alignment strategy. Firstly, we design alignment network (AN) and target network (TN) with the same structure.

As the annotation of remote sensing images requires domain expertise, it is difficult to construct a large-scale and accurate annotated dataset. Image-level annotation data learning has become a research hotspot. In addition, due to the difficulty in avoiding mislabeling, label noise cleaning is also a concern. In this paper, a semantic segmentation method for remote sensing images based on uncertainty perception with noisy labels is proposed. The main contributions are three-fold.

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users.
A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and