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Found 7 papers, 5 papers with code
Learning Situation Hyper-Graphs for Video Question Answering
The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction.
Ranked #6 on
Video Question Answering
on AGQA 2.0 balanced
(Average Accuracy metric)
Weakly Supervised Grounding for VQA in Vision-Language Transformers
Transformers for visual-language representation learning have been getting a lot of interest and shown tremendous performance on visual question answering (VQA) and grounding.
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone.
MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering
We present MMFT-BERT(MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities.
Analysis of Hand Segmentation in the Wild
In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets.
Segmenting Sky Pixels in Images
As a result of our efforts, we have seen an improvement of 10-15% in the average MCR compared to the prior methods on SkyFinder dataset.
Egocentric Height Estimation
We used both traditional computer vision approaches and deep learning in order to determine the visual cues that results in best height estimation.
