5 research outputs found
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA
Video Question Answering (Video QA) requires fine-grained understanding of
both video and language modalities to answer the given questions. In this
paper, we propose novel training schemes for multiple-choice video question
answering with a self-supervised pre-training stage and a supervised
contrastive learning in the main stage as an auxiliary learning. In the
self-supervised pre-training stage, we transform the original problem format of
predicting the correct answer into the one that predicts the relevant question
to provide a model with broader contextual inputs without any further dataset
or annotation. For contrastive learning in the main stage, we add a masking
noise to the input corresponding to the ground-truth answer, and consider the
original input of the ground-truth answer as a positive sample, while treating
the rest as negative samples. By mapping the positive sample closer to the
masked input, we show that the model performance is improved. We further employ
locally aligned attention to focus more effectively on the video frames that
are particularly relevant to the given corresponding subtitle sentences. We
evaluate our proposed model on highly competitive benchmark datasets related to
multiple-choice video QA: TVQA, TVQA+, and DramaQA. Experimental results show
that our model achieves state-of-the-art performance on all datasets. We also
validate our approaches through further analyses.Comment: Accepted at AAAI 202
Multimodal and Embodied Learning with Language as the Anchor
Since most worldly phenomena can be expressed via language, language is a crucial medium for transferring information and integrating multiple information sources. For example, humans can describe what they see, hear and feel, and also explain how they move with words. Conversely, humans can imagine scenes, sounds, and feelings, and move their body from language descriptions. Therefore, language plays an important role in solving machine learning (ML) and artificial intelligence (AI) problems with multimodal input sources. This thesis studies how different modalities can be integrated with language in multimodal learning settings as follows. First, we explore the possibility to integrate external information from the textual description about an image into a visual question answering system which integrates the key words/phrases in paragraph captions in semi-symbolic form, to make the alignment between features easier. We expand the direction to a video question answering task. We employ dense captions, which generate object-level descriptions of an image, to help localize the key frames in a video clip for answering a question. Next, we build benchmarks to evaluate embodied agents to perform tasks according to natural language instruction from humans. We introduce a new instruction-following navigation and object assembly system, called ArraMon in which agents follow the natural language instructions to collect an object and put it in a target location, requiring agents to deeply understand referring expressions and the concept of direction from the egocentric perspective. We also suggest a new task setup for the useful Cooperative Vision-and-Dialog Navigation (CVDN) dataset. We analyze scoring behaviors of models and find issues from the existing Navigation from Dialog History (NDH) task and propose a more realistic and challenging task setup, called NDH-Full which better appreciates the purpose of the CVDN dataset. Finally, we explore AI assistant systems which help humans with different tasks. We introduce a new correctional captioning dataset on human body pose, called FixMyPose, to encourage the ML/AI community to build such guidance systems that require models to learn to distinguish different levels of pose difference to describe desirable pose change. Also, we introduce a new conversational image search and editing assistant system, called CAISE, in which an agent helps a user to search images and edit them by holding a conversation.Doctor of Philosoph