Semantic curiosity for active visual learning
WebMay 7, 2024 · This intrinsic motivation and curiosity enables the policy to obtain useful data automatically. The researchers conducted extensive experiments to validate the utility of the learned representations on downstream tasks such as semantic navigation, visual language navigation and real image understanding. WebAug 25, 2024 · Semantic Curiosity for Active Visual Learning (ECCV-2024, spotlight) Short presentation for the ECCV-2024 paper, "Semantic Curiosity for Active Visual Learning". …
Semantic curiosity for active visual learning
Did you know?
WebWe use semantic curiosity to learn an exploration policy on set of the training environments. The exploration policy is learned by projecting segmentation masks on the top-down view … WebTABLE I COMPARISON WITH THE STATE-OF-THE-ART METHODS FOR OBJECT DETECTION (BBOX) AND INSTANCE SEGMENTATION (SEGM) USING AP50 AS THE METRIC. N MEANS THE EXPLORATION POLICY IS PROGRESSIVELY TRAINED FOR N TIMES. - "Learning to Explore Informative Trajectories and Samples for Embodied Perception"
WebOur framework called Self-supervised Embodied Active Learning (SEAL) consists of two phases, Action, where we learn an active exploration policy, and Perception, where we train the Perception Model on data gathered using the exploration policy and labels obtained using spatio-temporal label propagation. WebThe exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible …
WebDec 28, 2024 · The purpose of the agents is to recognize objects and other semantic classes in the whole building at the end of a process that combines exploration and active visual learning. As we study this task in a lifelong learning context, the agents should use knowledge gained in earlier visited environments in order to guide their exploration and ... WebIn this paper, we study the task of embodied interactive learning for object detection. Given a set of environments (and some labeling budget), our goal is to learn an object detector by having an agent select what data to obtain labels for. How should an exploration policy decide which trajectory should be labeled? One possibility is to use a trained object …
WebJun 16, 2024 · Our semantic curiosity policy is based on a simple observation -- the detection outputs should be consistent. Therefore, our semantic curiosity rewards …
http://www.aas.net.cn/article/doi/10.16383/j.aas.c220564 editing dynamic range in lightroomWebJun 16, 2024 · The exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other … cons about solar powerWebReinforcement learning module [3] (input) current semantic map à(output) long-term goal Reward: newly explored area •Short-term goal: ... Chaplot, Devendra Singh, et al. "Semantic curiosity for active visual learning."ECCV, 2024. [2] Liu, Ze, et al. "Swin transformer: Hierarchical vision transformer using shifted windows."arXiv,2024. consall shootWebMay 7, 2024 · This intrinsic motivation and curiosity enables the policy to obtain useful data automatically. The researchers conducted extensive experiments to validate the utility of the learned... consall occinvestment on bank statementWebThe exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible … editing eaglesWebWe define semantic curiosity as the temporal inconsistency in object detection and segmentation predictions from the current model. We use a Mask RCNN to obtain the object predictions. In order to associate the predictions across frames in a trajectory, we use a semantic mapping module as described below. Semantic Mapping. consall team kftWebApr 13, 2024 · [2] Chaplot D S, Jiang H, Gupta S, et al. Semantic curiosity for active visual learning[C]//Computer Vision–ECCV 2024: 16th European Conference, Glasgow, UK, August 23–28, 2024, Proceedings, Part VI 16. Springer International Publishing, 2024: 309-326. consall woods rspb