Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users nee...
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The underwater environment is complex and diverse, making it challenging to locate aquatic organisms accurately. The precise identification of underwater animals is crucial for ecological research and fisheries manage...
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Human activity recognition is a crucial domain in computerscience and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, ...
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Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...
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Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
In recent years, infrared target detection has played a crucial role in intelligent transportation and assisted driving. Addressing the current issues of low detection accuracy, poor robustness, and missed detections ...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like ...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small *** address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image *** adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different *** strengthens overlooked image details,extending the IAEN module’s *** the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation *** entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network *** lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image *** experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
The dominance scoring problem in a multidimensional dataset is to return the number of points dominated by a given point, which is a common metric for evaluating the quality of a data point. Dominance scoring is an el...
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Plaintext-checkable encryption (PCE) can support searches over ciphertext by directly using plaintext. The functionality of a search is modeled by a specific check algorithm that takes a pair of target plaintext and c...
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A nonisolated fixed-ratio resonant switched-capacitor converter (RSCC) with high peak efficiency and high power density is encouraged as intermediate bus converter. With higher input voltages, stacked topologies achie...
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