Our approach named as CycleMatch can maintain both inter-modal correlations and intra-modal consistency by cascading dual mappings and reconstructed mappings in a cyclic fashion. Moreover, in order to achieve a robust inference, we propose to employ two late-fusion approaches: average fusion and ...
“分类”的方法则是希望用神经网络去拟合一个比余弦相似度更好的函数,来对输入的 image 和 text feature 进行 match or not 的二分类,这种输入来自两个模态,输出为某结果的神经网络设计一般称为 Fusion,在 VQA 中比较常见,代表方法有MTFN[3]。 好,铺垫完毕。本部分的内容是讨论传统的相似度计算和 Fusion 的...
Given a textual graph G1 = (V1, E1) of a text, and a visual graph G2 = (V2, E2) of an image, our goal is tomatch two graphsto learn fine-grained correspondence, producing similarity g(G1, G2) asglobal similarityof an image-textpair. ☆Concretely, we first compute thesimilarities...
和之前的非预训练模型多采用max triplet loss优化不同,UNITER所用的模型优化策略是对每个文本对分别构造2个random negatives和2个hard negatives,然后计算而分类的match loss。 Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks (ECCV 2020) 模型介绍:经典的OSCAR模型也是单流结构,但它的输入...
Given a textual graph G1 = (V1, E1) of a text, and a visual graph G2 = (V2, E2) of an image, our goal is to match two graphs to learn fine-grained correspondence, producing similarity g(G1, G2) asglobal similarityof an image-text pair. ...
But after adding the relational graph module, it can successfully identifies the correct match ranked first. According to the query results in Fig. 5 and 6, it can be more clearly verified that when the relational graph module and the negative sample module are added, both image query and ...
Finally, we discuss the challenges and the future trends in image–text match- ing. Although remarkable studies have been achieved in matching task, it still needs more work to achieve perfor- mance that can mimic human behavior. We look forward to help junior researchers to understand the ...
caffe-recurrent/ - extract_feature.py # extract features using VGG-16 Tensorflow/ -BidirectionNet_tfidf.py # train using the tfidf features -BidirectionNet_lstm.py # train lstm -test_match_pairList.py # generate the matching result (top-10) using image/text embeddings. NLP/ -run_SH_...
Look, imagine and match: improving textual-visual cross-modal retrieval with genera-tive models[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Jun 18-22, 2018. Piscataway: IEEE, 2018: 7181-7189.. Google Scholar [66] WANG B K, ...
Cyclematch: a cycle-consistent embedding network for image-text matching Pattern Recognit. (2019) H. Wang et al. Stacked squeeze-and-excitation recurrent residual network for visual-semantic matching Pattern Recognit. (2020) X. Xiao et al. Dense semantic embedding network for image captioning Patte...