  Grounded Situation Recognition (GSR) is capable of recognizing and
interpreting visual scenes in a contextually intuitive way, yielding salient
activities (verbs) and the involved entities (roles) depicted in images. In
this work, we focus on the application of GSR in assisting people with visual
impairments (PVI). However, precise localization information of detected
objects is often required to navigate their surroundings confidently and make
informed decisions. For the first time, we propose an Open Scene Understanding
(OpenSU) system that aims to generate pixel-wise dense segmentation masks of
involved entities instead of bounding boxes. Specifically, we build our OpenSU
system on top of GSR by additionally adopting an efficient Segment Anything
Model (SAM). Furthermore, to enhance the feature extraction and interaction
between the encoder-decoder structure, we construct our OpenSU system using a
solid pure transformer backbone to improve the performance of GSR. In order to
accelerate the convergence, we replace all the activation functions within the
GSR decoders with GELU, thereby reducing the training duration. In quantitative
analysis, our model achieves state-of-the-art performance on the SWiG dataset.
Moreover, through field testing on dedicated assistive technology datasets and
application demonstrations, the proposed OpenSU system can be used to enhance
scene understanding and facilitate the independent mobility of people with
visual impairments. Our code will be available at
https://github.com/RuipingL/OpenSU.
