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Found 41 papers, 14 papers with code
PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions
This paper describes our system used in the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard.
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
Fast Peer Adaptation with Context-aware Exploration
To do so, it is crucial for the agent to efficiently probe and identify the peer's strategy, as this is the prerequisite for carrying out the best response in adaptation.
DeCoF: Generated Video Detection via Frame Consistency
The escalating quality of video generated by advanced video generation methods leads to new security challenges in society, which makes generated video detection an urgent research priority.
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks.
Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation
In this work, we try to arouse the potential of enhancer + detector.
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
To overcome these limitations, we propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration
Both stages directly estimate the respective target deformation fields.
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations.
Ranked #16 on
Thermal Image Segmentation
on MFN Dataset
Bilevel Generative Learning for Low-Light Vision
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on
Semantic Segmentation
on FMB Dataset
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes (i. e., freezing the encoder in the adaptation and testing phases).
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting
Real-world complex acoustic environments especially the ones with a low signal-to-noise ratio (SNR) will bring tremendous challenges to a keyword spotting (KWS) system.
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes
3D object detection plays a crucial role in numerous intelligent vision systems.
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Recently, multi-modality scene perception tasks, e. g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems.
PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information
The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages.
Reporting delays: a widely neglected impact factor in COVID-19 forecasts
Motivated by the desire to enhance epidemic forecasts, we develop a statistical framework to detect, uncover, and remove reporting delays in the infectious, recovered, and deceased epidemic time series.
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model
In this work, we propose a two-stage knowledge distillation method to solve these two problems: the first step is to make the big and non-streaming teacher model smaller, and the second step is to make it streaming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Two Stage Contextual Word Filtering for Context bias in Unified Streaming and Non-streaming Transducer
In this paper, we propose an efficient approach to obtain a high quality contextual list for a unified streaming/non-streaming based E2E model.
Practical Exposure Correction: Great Truths Are Always Simple
Improving the visual quality of the given degraded observation by correcting exposure level is a fundamental task in the computer vision community.
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement.
Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation
In this paper, we design an adjacent slice feature fusion model to introduce information from adjacent slices.
Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR
Then, during the training of the conversational ASR system, the extractor will be frozen to extract the textual representation of preceding speech, while such representation is used as context fed to the ASR decoder through attention mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Toward Fast, Flexible, and Robust Low-Light Image Enhancement
Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios.
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling
Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2. 0 models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Conversational Speech Recognition By Learning Conversation-level Characteristics
Conversational automatic speech recognition (ASR) is a task to recognize conversational speech including multiple speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model
In our framework, the encoder is initialized with a pretrained AM (wav2vec2. 0).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement
Further, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short).
Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision
To partially address above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, which not only can address low-light enhancement task, but also has the flexibility to handle other more challenging downstream vision applications.
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning
Under such a framework, the neural network is usually pre-trained with massive unlabeled data and then fine-tuned with limited labeled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning
For the former task, a standard deviation constraint loss (SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents under the same language.
Improving Speech Recognition Accuracy of Local POI Using Geographical Models
Secondly, a group of geo-specific language models (Geo-LMs) are integrated into our speech recognition system to improve recognition accuracy of long tail and homophone POI.
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
Low-light image enhancement plays very important roles in low-level vision field.
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition
Experiments on AISHELL-1 data show that the proposed model, along with the training strategies, improve the character error rate (CER) of MoChA from 8. 96% to 7. 68% on test set.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments.
Task-Oriented Convex Bilevel Optimization with Latent Feasibility
This paper firstly proposes a convex bilevel optimization paradigm to formulate and optimize popular learning and vision problems in real-world scenarios.
A Bridging Framework for Model Optimization and Deep Propagation
Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios.
Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-convex Optimization
Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems.
Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement
Enhancing visual qualities of images plays very important roles in various vision and learning applications.
