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Recurrently refining the optical flow based on a single highresolution feature demonstrates high performance. We exploit the strength of this strategy to build a novel architecture for the joint learning of optical flow and depth. Our proposed architecture is improved to work in the case of training on unlabeled data, which is extremely challenging. The loss is computed for the iterations carried out over a single high-resolution feature, where the reconstruction loss fails to optimize the accuracy particularity in occluded regions.

Deploying style transfer methods on resource-constrained devices is challenging, which limits their real-world applicability. To tackle this issue, we propose using pruning techniques to accelerate various visual style transfer methods. We argue that typical pruning methods may not be well-suited for style transfer methods and present an iterative correlation-based channel pruning (ICCP) strategy for encoder-transform-decoder-based image/video style transfer models.

In weakly supervised video anomaly detection, it has been verified that anomalies can be biased by background noise. Previous works attempted to focus on local regions to exclude irrelevant information. However, the abnormal events in different scenes vary in size, and current methods struggle to consider local events of different scales concurrently. To this end, we propose a multi-scale integrated perception

Semi-supervised learning (SSL) is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation (MoS). However, most existing SSL methods predict pixels in a single image independently, ignoring the relations among images and categories. In this paper, we propose a two-stage Dual Contrastive Learning Network (DCL-Net) for semi-supervised MoS, which utilizes global and local contrastive learning to strengthen the relations among images and classes.

In this paper, we present the decomposed triplane-hash neural radiance fields (DT-NeRF), a framework that significantly improves the photorealistic rendering of talking faces and achieves state-of-the-art results on key evaluation datasets. Our architecture decomposes the facial region into two specialized triplanes: one specialized for representing the mouth, and the other for the broader facial features. We introduce audio features as residual terms and integrate them as query vectors into our model through an audio-mouthface transformer.

Previous works have shown that perturbing internal-layer features can significantly enhance the transferability of black-box attacks in classifiers. However, these methods have not achieved satisfactory performance when applied to detectors due to the inherent differences in features between detectors and classifiers. In this paper, we introduce a concise and practical untargeted adversarial attack in a label-free manner, which leverages only the feature extracted from the backbone model.

Recognizing human feelings from image and text is a core
challenge of multi-modal data analysis, often applied in personalized advertising. Previous works aim at exploring the
shared features, which are the matched contents between
images and texts. However, the modality-dependent sentiment information (private features) in each modality is usually ignored by cross-modal interactions, the real sentiment
is often reflected in one modality. In this paper, we propose a Modality-Dependent Sentiment Exploring framework

Due to the different physical imaging models, most haze or rain removal methods for daytime images are not suitable for nighttime images. Fog effect produced by the accumu-lation of rain also brings great challenges to the restoration of lowlight nighttime images. To deal well with the multi-ple noise interference in this complex situation, we propose a saliency-guided dynamic restoration network (SDRNet) that can remove rain and haze in nighttime scenes. First, a saliency-guided detail enhancement preprocessing method is designed to get images with clearer details as the auxilia-ry input.

The compressed sensing MRI aims to recover high-fidelity images from undersampled k-space data, which enables MRI acceleration and meanwhile mitigates problems caused by prolonged acquisition time, such as physiological motion artifacts, patient discomfort, and delayed medical care. In this regard, the deep unfolding network (DUN) has emerged as the predominant solution due to the benefits of better interpretability and model capacity. However, existing algorithms remain inadequate for two principal reasons.

The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement. In this paper, we introduce TokenMotion (TMNet), which employs a transformer-based model to enhance VCOD by extracting motion-guided features using a learnable token selection. Evaluated on the challenging MoCA-Mask dataset, TMNet achieves state-of-the-art performance in VCOD.