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Found 7 papers, 5 papers with code
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Specifically, we propose Class-Balance-based sample Selection (CBS) to prevent the tail class samples from being neglected during training.
Hierarchical Graph Pattern Understanding for Zero-Shot VOS
However, existing optical flow-based methods have a significant dependency on optical flow, which results in poor performance when the optical flow estimation fails for a particular scene.
Holistic Prototype Attention Network for Few-Shot VOS
Existing methods have demonstrated that the domain agent-based attention mechanism is effective in FSVOS by learning the correlation between support images and query frames.
Co-attention Propagation Network for Zero-Shot Video Object Segmentation
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects.
Attention Map Guided Transformer Pruning for Edge Device
Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Towards Automatic Construction of Diverse, High-quality Image Dataset
To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.
