Low recall when testing on flickr30k-cn dataset

[Qiulin-W]

Hi, thanks for the great work!

I tested the pretrained model for zero-shot img2text and text2img retrieval on flickr30k-cn validation set. The bboxes are obtained as indicated in https://github.com/chuhaojin/BriVL-BUA-applications. For each image, we only select the one caption with the highest fluency score. However, the recall@1 for the two task is only 15.93% and 13.74%, respectively. The same evaluation for ViLT reaches 73.2% and 55.0%. I'm wondering whether you test on this dataset? Any comments on my results?

p.s. An example json file of the dataset is as follows
{"sentences": [["0", "一个小男孩正在玩呼啦圈。"]], "bbox": [[78, 92, 183, 124], [179, 137, 363, 214], [68, 21, 170, 101], [73, 326, 206, 498], [338, 150, 379, 187], [0, 305, 363, 396], [105, 273, 179, 342], [30, 32, 261, 483], [89, 192, 130, 210], [12, 155, 389, 498], [173, 150, 192, 167], [17, 134, 237, 353], [10, 341, 389, 496], [90, 76, 170, 169], [29, 118, 282, 363], [17, 357, 339, 402], [129, 133, 152, 155], [6, 423, 78, 498], [97, 231, 138, 250], [74, 22, 174, 175], [165, 167, 197, 191], [34, 77, 242, 494], [316, 145, 341, 197], [33, 167, 164, 323], [294, 1, 382, 19], [199, 8, 382, 158], [15, 385, 389, 497], [1, 366, 379, 396], [179, 126, 371, 228], [204, 13, 379, 130], [57, 23, 189, 235], [59, 71, 230, 482], [55, 23, 203, 167], [44, 29, 213, 248], [61, 27, 210, 219], [32, 124, 264, 367], [44, 39, 236, 286], [18, 326, 338, 445], [198, 383, 389, 496], [61, 344, 209, 498], [95, 269, 186, 340], [46, 302, 331, 471], [19, 123, 344, 307], [11, 14, 374, 409], [31, 132, 234, 357], [20, 134, 271, 354], [16, 10, 358, 360], [32, 20, 297, 478], [39, 19, 206, 157], [2, 330, 62, 443], [29, 168, 175, 331], [153, 312, 389, 404], [2, 408, 272, 498], [0, 328, 347, 467], [317, 148, 349, 197], [35, 302, 227, 458], [38, 143, 229, 366], [11, 367, 385, 492], [191, 320, 380, 389], [323, 148, 347, 199], [61, 324, 244, 498], [79, 0, 385, 495], [47, 143, 222, 355], [6, 0, 389, 221], [0, 367, 377, 407], [0, 194, 389, 498], [103, 123, 356, 222], [14, 7, 222, 183], [20, 4, 389, 164], [0, 286, 389, 497], [14, 4, 191, 132], [21, 331, 308, 438], [59, 118, 352, 219], [70, 88, 181, 128], [0, 227, 389, 498], [4, 327, 389, 490], [0, 330, 363, 451], [15, 348, 302, 436], [126, 116, 156, 147], [48, 52, 269, 480], [17, 0, 224, 154], [34, 54, 245, 478], [8, 98, 389, 491], [24, 12, 167, 110], [17, 116, 316, 361], [32, 0, 305, 476], [4, 110, 37, 201], [48, 135, 223, 349], [14, 410, 370, 497], [38, 13, 265, 391], [51, 301, 219, 483], [54, 332, 244, 484], [22, 127, 256, 356], [47, 172, 216, 360], [81, 92, 178, 124], [75, 82, 174, 140], [27, 150, 230, 361], [53, 20, 192, 152], [0, 269, 356, 357], [18, 2, 195, 118]], "image_id": "/export/PTM_dataset/flickr30k-cn/flickr30k-images/2954461906.jpg"}
{"sentences": [["0", "妇女们正在喝酒和编织。"]], "bbox": [[74, 113, 383, 271], [451, 159, 499, 273], [6, 20, 75, 106], [5, 16, 114, 277], [0, 7, 481, 251], [434, 195, 454, 221], [353, 34, 478, 264], [217, 8, 320, 161], [287, 127, 317, 209], [376, 15, 439, 72], [28, 260, 84, 277], [163, 12, 245, 154], [333, 163, 465, 269], [115, 152, 196, 195], [147, 3, 179, 78], [440, 49, 499, 185], [293, 182, 321, 211], [198, 136, 237, 180], [241, 8, 291, 58], [325, 139, 344, 178], [394, 126, 411, 149], [2, 205, 320, 277], [1, 70, 93, 197], [210, 125, 228, 156], [123, 95, 141, 152], [146, 0, 499, 65], [162, 6, 324, 152], [167, 50, 237, 131], [16, 167, 90, 274], [51, 0, 149, 80], [0, 64, 100, 233], [111, 139, 184, 181], [385, 63, 452, 151], [230, 54, 302, 138], [378, 50, 490, 264], [18, 180, 88, 266], [54, 142, 80, 163], [65, 259, 85, 277], [6, 9, 80, 112], [162, 53, 396, 151], [177, 11, 486, 254], [397, 94, 494, 267], [121, 89, 141, 148], [5, 4, 111, 277], [165, 6, 244, 149], [423, 58, 499, 254], [336, 12, 477, 273], [338, 14, 465, 258], [83, 84, 144, 142], [119, 16, 440, 163], [293, 160, 319, 214], [9, 162, 90, 270], [9, 16, 120, 277], [441, 157, 499, 272], [111, 142, 188, 184], [164, 14, 491, 271], [15, 174, 137, 275], [7, 32, 139, 276], [5, 0, 114, 277], [347, 120, 494, 277], [4, 12, 126, 277], [213, 5, 309, 161], [429, 35, 494, 175], [88, 209, 319, 276], [140, 0, 499, 75], [222, 6, 305, 153], [6, 8, 106, 277], [340, 90, 492, 277], [108, 123, 401, 274], [95, 1, 488, 268], [434, 157, 499, 271], [347, 214, 452, 274], [114, 88, 147, 154], [157, 14, 251, 154], [48, 139, 257, 271], [194, 128, 238, 181], [80, 120, 384, 273], [169, 47, 233, 133], [170, 43, 235, 133], [346, 12, 470, 195], [54, 6, 451, 244], [12, 1, 161, 88], [67, 195, 350, 275], [345, 170, 469, 269], [379, 23, 484, 201], [350, 213, 475, 273], [6, 13, 67, 109], [60, 85, 328, 266], [7, 2, 338, 263], [293, 127, 314, 203], [11, 11, 84, 107], [211, 13, 463, 205], [342, 79, 496, 274], [71, 15, 483, 169], [198, 132, 233, 175], [54, 104, 384, 269], [161, 9, 246, 152], [367, 181, 478, 270], [93, 1, 499, 103], [16, 190, 366, 276]], "image_id": "/export/PTM_dataset/flickr30k-cn/flickr30k-images/2314492671.jpg"}

[YizhaoGao-AI]

Hi, there are many reasons: 1. Our model is pre-trained on weak semantic correlation data crawled from the web while ViLT is pre-trained on strong semantic correlation data. Flickr30K is also a strong correlation dataset. 2. The translation of Flickr30K inevitably brings negative effects.