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Found 8 papers, 2 papers with code
Referring Expression Comprehension Using Language Adaptive Inference
Different from universal object detection, referring expression comprehension (REC) aims to locate specific objects referred to by natural language expressions.
SVoice: Enabling Voice Communication in Silence via Acoustic Sensing on Commodity Devices
The design of introduces a new model that provides the unique mapping relationship between ultrasound and speech signals, so that the audible speech can be successfully reconstructed from the silent speech.
When Video Classification Meets Incremental Classes
With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining the knowledge of old videos with limited storage and computing resources.
Memory Efficient Class-Incremental Learning for Image Classification
To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer.
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network.
MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning
As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge.
Semantic Neighborhood-Aware Deep Facial Expression Recognition
Different from many other attributes, facial expression can change in a continuous way, and therefore, a slight semantic change of input should also lead to the output fluctuation limited in a small scale.
Facial Expression Recognition
Facial Expression Recognition (FER)
DAPnet: A Double Self-attention Convolutional Network for Point Cloud Semantic Labeling
The incorporation of the double self-attention module has an average of 7\% improvement on the pre-class accuracy.
