DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations (Supplementary Material) Ximeng Sun 1 Ping Hu1 Kate Saenko Boston University, 2

In this section, we provide the average per-class and average overall precisions (CP and OP), recalls (CR and oR) and F1 scores (CF1 and OF1) of DualCoOp in the experiment of MLR with Partial Labels on MS-COCO [3], VOC2007 [2] and BigEarth [1] (see Table 3, 4 and 5 in supplementary material) as a supplementary for Table?? and?? in the main paper. We have visualized the class-specific region feature aggregation on MS-COCO dataset (in Figure 1). We can see DualCoOp generates the high attention score at the correct objects. The pascal visual object classes (voc) challenge.