VQA is like training the computer to not only "see" the visual elements but also to "understand" and "speak" about them when prompted with questions. For example, you could ask questions like: How many forklifts are in an image?
The task is about training models in a end-to-end fashion on a multimodal dataset made of triplets: animagewith no other information than the raw pixels, aquestionabout visual content(s) on the associated image, a shortanswerto the question (one or a few words). ...
I used the pre-trained model ResNet-50 with the last layer (the Softmax layer) removed, and added a Softmax layer with different answers as classes. I used the VQA-Med 2019 and VQA-Med 2020 training datasets to train my models. In the VQG task, I presented a variational autoencoder...
Our paper presents a method to answer questions about regions by using localized attention. In localized attention, a target region can be given to the model so that answers are focused on a user-defined region.🔥 Repo updatesData download Training Inference Metrics plotting Running the code in...
比如不同的图片类型, 难度分级，图片在问题中的位置。而且更干净，更适合用做training，short answer/...
We train a multi-label linear classifier (i.e. MLP with one hidden layer and sigmoid activation function) on top of BERT (row d), ResNet (row i), and CLIP (rows e/j/m) representations to score answers from the vocabulary. When questions and images are both provided, we first concate...
if we are working withOK-VQAor A-OKVQA datasets have been annotated with around one question per image in the training set, augmenting them with two or three questions will suffice. Another method would be a soft truncation to allocate the required question multiplicity image-wise. This approac...
- no maximum input length constraint Experiment 数据集 : the validation set (214,354 questions) and test-dev set (107,394 questions) of VQA-v2 the test set (5,046 questions) of OK-VQA the test-dev set (12,578 questions) of GQA-balanced ...
Pre-training Objectives In the BERT masked language modeling objective, 15% of the input text tokens are first replaced with either a special [MASK] token, a random token or the original token, at random with chances equal to 80%, 10%, and 10%, respectively. Then, at the model output,...
The test set of VQA-v2 is not publicly available and requires exact matches of the answers, making open-world answers and LLM-based graders inapplicable. We instead adopt the VQA-v2 rest-val dataset, the validation dataset in BEiT-3 and VLMo that was never used for training. It contains ...