Microsoft and Google’s research groupstied for first placein the recentMS COCO Image Captioning Challenge 2015. The winners were decided based on two main metrics: The share of captions that were equal to or b
Microsoft COCO is a new image recognition, segmentation, and captioning dataset that is designed to recognize multiple objects and sections of an image while distinguishing their unique context. The dataset can create five separate descriptions of the image which has several uses, though the most obv...
(based on Faster R-CNN) proposes image regions, each with an associated feature vector, while the top-down mechanism determines feature weightings. Applying this approach to image captioning, our results on the MSCOCO test server establish a new state-of-the-art for the ...
Image Captioning based on Bottom-Up and Top-Down Attention model deep-learningpytorchimage-captioningattention-modelmicrosoft-coco UpdatedJan 3, 2019 Jupyter Notebook SpongeBab/COCO_only_person Star13 Code Issues Pull requests Use the python script to select images contains person in the COCO。
To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and ...
The challenge of emotion-aware image commenting Caption generation is a core element of the AI image(video)-to-text domain. Much of the research in this field has focused on enabling machines to detect and characterize objects in images. Existing deep learning-based image captioning methods extrac...
[2023.02.28]We released theSGinW benchmarkfor our challenge. Welcome to build your own models on the benchmark! [2023.02.27]Our X-Decoder has been accepted by CVPR 2023! [2023.02.07]We combine(strong image understanding),(strong language understanding) and(strong image generation) to make an...
It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no ad...
microsoft/unilmPublic NotificationsYou must be signed in to change notification settings Fork2.6k Star21.3k Files master Sign in to see the full file tree. beit3 utils.py Latest commit addf400 fix beit-3 Apr 11, 2024 1a10544·Apr 11, 2024 ...
generation tasks, includingVisual Question Answering (VQA),Graph Question Answering (GQA),Natural Language Visual Reasoning for Real (NLVR2),Image-Text Retrieval,Text-Image Retrieval,Image Captioning on COCO dataset, andNovel Object Captioning (NoCaps). The overall setting is illustrated...