336 research outputs found
Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local operation computes the response at a position as a
weighted sum of the features at all positions. This building block can be
plugged into many computer vision architectures. On the task of video
classification, even without any bells and whistles, our non-local models can
compete or outperform current competition winners on both Kinetics and Charades
datasets. In static image recognition, our non-local models improve object
detection/segmentation and pose estimation on the COCO suite of tasks. Code is
available at https://github.com/facebookresearch/video-nonlocal-net .Comment: CVPR 2018, code is available at:
https://github.com/facebookresearch/video-nonlocal-ne
Towards Interaction-level Video Action Understanding
A huge amount of videos have been created, spread, and viewed daily. Among these massive videos, the actions and activities of humans account for a large part. We desire machines to understand human actions in videos as this is essential to various applications, including but not limited to autonomous driving cars, security systems, human-robot interactions and healthcare. Towards real intelligent system that is able to interact with humans, video understanding must go beyond simply answering ``what is the action in the video", but be more aware of what those actions mean to humans and be more in line with human thinking, which we call interactive-level action understanding. This thesis identifies three main challenges to approaching interactive-level video action understanding: 1) understanding actions given human consensus; 2) understanding actions based on specific human rules; 3) directly understanding actions in videos via human natural language. For the first challenge, we select video summary as a representative task that aims to select informative frames to retain high-level information based on human annotators' experience. Through self-attention architecture and meta-learning, which jointly process dual representations of visual and sequential information for video summarization, the proposed model is capable of understanding video from human consensus (e.g., how humans think which parts of an action sequence are essential). For the second challenge, our works on action quality assessment utilize transformer decoders to parse the input action into several sub-actions and assess the more fine-grained qualities of the given action, yielding the capability of action understanding given specific human rules. (e.g., how well a diving action performs, how well a robot performs surgery) The third key idea explored in this thesis is to use graph neural networks in an adversarial fashion to understand actions through natural language. We demonstrate the utility of this technique for the video captioning task, which takes an action video as input, outputs natural language, and yields state-of-the-art performance. It can be concluded that the research directions and methods introduced in this thesis provide fundamental components toward interactive-level action understanding
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