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Found 10 papers, 3 papers with code
Denoising-Diffusion Alignment for Continuous Sign Language Recognition
As a key to social good, continuous sign language recognition (CSLR) aims to promote active and accessible communication for the hearing impaired.
Ranked #2 on
Sign Language Recognition
on CSL-Daily
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition
Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the overall model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained.
Ranked #4 on
Sign Language Recognition
on CSL-Daily
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions
Then, we propose a novel core-failure-set guided DARTS that embeds a K-center-greedy algorithm for DARTS to select suitable corrupted failure examples to refine the model architecture.
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem
To this, we model the traffic control problem as a partially observable weak cooperative traffic model (PO-WCTM) to optimize the overall traffic situation of a group of intersections.
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency.
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions
Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuous-action domains, multiagent coordination domains with continuous actions have received relatively few investigations.
SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment.
