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Found 4 papers, 3 papers with code
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
Expanding Low-Density Latent Regions for Open-Set Object Detection
Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability.
Few-Shot Object Detection via Variational Feature Aggregation
As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples.
Distributed Attention for Grounded Image Captioning
We study the problem of weakly supervised grounded image captioning.
