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Found 12 papers, 5 papers with code
Continual Learning via Explicit Structure Learning
During structure learning, the model optimizes for the best structure for the current task.
Decomposition-based and Interference Perception for Infrared and Visible Image Fusion in Complex Scenes
Infrared and visible image fusion has emerged as a prominent research in computer vision.
Physical Perception Network and an All-weather Multi-modality Benchmark for Adverse Weather Image Fusion
To bridge this research gap, we proposed an all-weather MMIF model.
SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection
Existing multi-focus image fusion (MFIF) methods often fail to preserve the uncertain transition region and detect small focus areas within large defocused regions accurately.
Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image Fusion
However, the fusion of multiple visible images with different focal regions and infrared images is a unprecedented challenge in real MMIF applications.
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR
To address this issue, we propose the integration of a novel dynamic contextual carry-over mechanism in a state-of-the-art (SOTA) unified ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Masked Audio Text Encoders are Effective Multi-Modal Rescorers
Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
Overall, our proposed model reduces the degradation of the streaming mode over the non-streaming full-contextual model from 41. 7% and 45. 7% to 16. 7% and 26. 2% on the LibriSpeech test-clean and test-other datasets respectively, while improving by a relative 15. 5% WER over the previous state-of-the-art unified model.
Attentive Normalization
In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous.
Ranked #71 on
Instance Segmentation
on COCO minival
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks.
AOGNets: Compositional Grammatical Architectures for Deep Learning
This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs.
Towards Interpretable R-CNN by Unfolding Latent Structures
We focus on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations.
