Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality.

Usage Application The model is intended for open-vocabulary object detection.Known Caveats

Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency.

Machine learning models--including prominent zero-shot models--are often trained on datasets whose labels are only a small proportion of a larger label space. Such spaces are commonly equipped with a metric that relates the labels via distances between them.

However, data annotation for training MLC models becomes much more labor-intensive due to the correlated (hence non-exclusive) labels and a potentially large and sparse label space.

Problem(7)isNP-complete. Weshow that there exists apolynomial time reduction from the set cover problem to(7). We construct theM matrix according to the sets A1,...,Am (thei-thcolumnof M isthenonzeropatternof Ai).