In Defence of Negative Mining for Annotating Weakly Labelled Data

Efficient Image and Video Co-localization with Frank-Wolfe Algorithm

“…We also combine the two and present results for joint image-video co-localization. Following previous works in weakly supervised localization (WSL) [13,17,32,[43][44][45][46] and co-localization [47], we use the CorLoc evaluation metric, defined as the percentage of images correctly localized according to the PASCAL-criterion: area(Bp∩Bgt) area(Bp∪Bgt) > 0.5, where B p is the predicted box and B gt is the ground-truth box. All CorLoc results are given in percentages.…”

“…Our method (Co-localization) [44] (WSL) [46] (WSL) [45] (WSL) [43] (WSL) [17] Table 3. CorLoc results on PASCAL07 compared to previous methods for weakly supervised localization (WSL).…”

“…We also show our method compared to previous WSL methods in Table 3 for the PASCAL07 dataset [13]. In the WSL scenario, previous methods are also given negative data [13,17,32,[43][44][45][46], whereas our method is not. Here, we can see that although our method is able to work Table 4.…”

“…The co-localization problem is similar to co-segmentation [21-24, 38, 39, 50] and weakly supervised localization (WSL) [13,17,31,32,[43][44][45][46]49]. In contrast to co-segmentation, we seek to localize objects with bounding boxes rather than segmentations, which allows us to greatly decrease the number of variables in our problem.…”

“…In addition to pixels, these images and videos are often tagged with the visual concepts they contain, leading to a natural source of weakly labeled data. Recent research has studied ways of leveraging this data, such as weakly supervised localization [13,17,32,[43][44][45][46], co-segmentation [21,23,38], and co-localization [37,47].…”