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Found 11 papers, 4 papers with code
SDI-Net: Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement
However, these methods still do not fully exploit the interaction between left and right view information.
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning
In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i. e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories.
Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation
Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories.
Automatic Depression Detection via Learning and Fusing Features from Visual Cues
Depression is one of the most prevalent mental disorders, which seriously affects one's life.
Decoupled Low-light Image Enhancement
On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information.
Few-shot Learning with Global Relatedness Decoupled-Distillation
To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.
Few-shot Partial Multi-view Learning
The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn.
Positive Sample Propagation along the Audio-Visual Event Line
To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.
A Survey on Large-scale Machine Learning
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.
Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation
In SGCPNet, we propose the strategy of spatial-detail guided context propagation.
Distribution-Guided Local Explanation for Black-Box Classifiers
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.
