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- Dhingra, Naina, et al.
(författare)
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Language-Attention Modular-Network for Relational Referring Expression Comprehension in Videos
- 2022
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Ingår i: 2022 26th international conference on pattern recognition (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4103-4110
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Konferensbidrag (refereegranskat)abstract
- Referring expression (RE) for video domain describes the video using a natural language expression. Relational RE comprehension in a video domain localizes an object in relation to a distinguishing context object. Unlike object grounding in videos using REs, not much work has been done in videos using relational REs. In this paper, we focus on (1) relational RE comprehension for videos, and (2) demonstrating the significance of attention for the task. We propose a novel modular network based approach for relational RE comprehension in highly ambiguous settings for videos. We show the significance of the language attention in modular approach by: (1) Using two different networks, i.e., modATN consisting of attention mechanism, visual modules, and a natural language expression input, and modSTR consisting of visual modules, and structured input (subject, subject adjective, object, object adjective, action, relation); (2) Introducing a new dataset having structured RE for relation RE comprehension task in modSTR. Finally, we propose an optimised modular network that outperforms and shows significant improvements over the baseline networks.
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| 2. |
- Jain, Shipra, et al.
(författare)
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Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
- 2021
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Ingår i: Proceedings of the IEEE International Conference on Computer Vision. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 7406-7416
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Konferensbidrag (refereegranskat)abstract
- The state-of-the-art object detection and image classification methods can perform impressively on more than 9k classes. In contrast, the number of classes in semantic segmentation datasets is relatively limited. This is not surprising when the restrictions caused by the lack of labeled data and high computation demand for segmentation are considered. In this paper, we propose a novel training methodology to train and scale the existing semantic segmentation models for a large number of semantic classes without increasing the memory overhead. In our embedding-based scalable segmentation approach, we reduce the space complexity of the segmentation model's output from O
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