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Found 5 papers, 3 papers with code
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details.
Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment.
Knowing Where to Focus: Event-aware Transformer for Video Grounding
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries.
Probabilistic Prompt Learning for Dense Prediction
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models.
Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples.
