Download the pretrained BERT model (link). First update config_denoise.py with the correct data and pretrained model paths. The hierarchy above $ _C.data_dir $ should contain two folders. The first is region_feat_gvd_wo_bgd (downloaded in the …

Membership Inference on Text-to-image Diffusion Models via Conditional Likelihood Discrepancy (NeurIPS 2024). Use ft_mia.sh script to fine-tune the target and shadow models on two different training sets (COCO_MIA_Finetuning Data). We additionally provide the intermediate results of the MS-COCO dataset in Sec. 4.2 for validation.

2024年1月21日 · We propose a novel approach for generating varying-length image descriptions with inadequate training data. Our approach is based on solving two sub-problems. At first, we automatically generate long "synthetic" captions, termed self-generated captions.

Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on co.

Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on control-ling the caption length, i.e. a short and concise descrip-tion or a long and detailed one.

金泰敏（游戏ID：Clid），1999年7月7日出生于 韩国，韩国英雄联盟职业选手，司职 打野，曾效力于 QG 战队、 JDG 战队、 SKT 战队、 Gen.G 战队、 FPX 战队，现效力于 HLE 战队。 [28] 金泰敏于2017年开始参加职业比赛，在2017年取得全国电子竞技大赛亚军；在2018年取得全国电子竞技大赛冠军；在2019年取得LCK春季赛冠军、季中冠军赛四强、LCK夏季赛冠军、英雄联盟全球总决赛四强 [10-12]；在2020年取得LCK春季赛亚军、LCK夏季赛季军 [13-14]；在2021取 …

2023年9月7日 · 但影响最大的还是其中“未成年”的因素，韩国的性同意年龄是“19岁”，clid“性骚扰”事件曝光的过程中，第二位女性在社交平台上明确表明了自己18岁“未成年”的身份，这无疑进一步增大了这件事带来的后果。

Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on controlling the caption length, i.e. a short and concise description or a long and detailed one.

2023年12月31日 · Abstract: Controllable image captioning models generate human-like image descriptions, enabling some kind of control over the generated captions. This paper focuses on controlling the caption length, i.e. a short and concise description or a long and detailed one.

In this paper, we propose a Chunk-Level Intent Detection (CLID) framework, where we introduce a sliding window-based self-attention (SWSA) scheme for regional chunk intent detection. Based on the SWSA, an auxiliary task is introduced to identify the intent transition point in an utterance and obtain sub-utterances with a single intent.