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Found 28 papers, 4 papers with code
360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes
However, as the distinct projections make it less possible to directly transfer knowledge between domains, we then propose Reliable Panoramic Prototype Adaptation Module (RP2AM) to transfer knowledge at both prediction and prototype levels.
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
To this end, we propose a novel framework, called GoodSAM, that introduces a teacher assistant (TA) to provide semantic information, integrated with SAM to generate ensemble logits to achieve knowledge transfer.
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition
To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem.
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More
Then, we propose a conceptual reasoning-based uncertainty estimation module, which simulates the recognition process to enrich the semantic representation.
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All
To make this possible, we 1) construct a knowledge base of text embeddings with the help of LLMs and multi-modal LLMs; 2) adaptively build LLM-augmented class-wise embedding center on top of the knowledge base and encoded visual embeddings; 3) align all the embeddings to the LLM-augmented embedding center via contrastive learning to achieve a unified and balanced representation space.
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation
However, the distinct projection discrepancies between source and target domains impede the direct knowledge transfer; thus, we propose a panoramic prototype adaptation module (PPAM) to integrate panoramic prototypes from the extracted knowledge for adaptation.
Parametric Augmentation for Time Series Contrastive Learning
In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format.
PAC Learnability under Explanation-Preserving Graph Perturbations
It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation
It extracts entity features from the multi-modal representations powered by our specially constructed entity knowledge graph; 2) Attribute Fusion Branch adeptly preserves and processes the attributes.
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation
Due to the completely different characteristics of ViT and CNN and the long-existing capacity gap between teacher and student models in Knowledge Distillation (KD), directly transferring the cross-model knowledge is non-trivial.
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
An explanation function for GNNs takes a pre-trained GNN along with a graph as input, to produce a `sufficient statistic' subgraph with respect to the graph label.
CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation
Moreover, to enhance the ability to discriminate unseen categories, PLM consisting of pseudo labels and weight generation is designed.
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
The ability to detect objects in all lighting (i. e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving. Traditional RGB-based detectors often fail under such varying lighting conditions. Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection.
Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
The aim is to tackle the domain gaps caused by the style disparities and distortion problem from the non-uniformly distributed pixels of equirectangular projection (ERP).
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding
Accordingly, we first introduce a novel event encoder that subtly models the temporal information from events and meanwhile, generates event prompts for modality bridging.
A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation
In this paper, we strive to answer the question "how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and exchanging the reliable knowledge between them for semantic segmentation?"
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively.
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes.
Evaluation of the Synthetic Electronic Health Records
Generative models have been found effective for data synthesis due to their ability to capture complex underlying data distributions.
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students
To this end, we propose class-aware feature consistency distillation (CFCD) that first leverages the outputs of each student as the pseudo labels and generates class-aware feature (CF) maps for knowledge transfer between the two students.
Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
Synthesising Electronic Health Records: Cystic Fibrosis Patient Group
Deep generative models are effective data synthesisers due to their ability to capture complex underlying distributions.
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation
However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed.
A Graph-based Imputation Method for Sparse Medical Records
Our work can facilitate the diagnosis of novel diseases based on the clinical history of past events, with the potential to increase our understanding of the landscape of comorbidities.
Network Generation with Differential Privacy
Differential privacy is a gold standard for data privacy, and the introduction of the differentially private stochastic gradient descent (DP-SGD) algorithm has facilitated the training of private neural models in a number of domains.
Fröhlich Condensate of Phonons in Optomechanical Systems
We propose that the optomechanical systems can be potential platforms to implement the Fr\"{o}hlich condensate of phonons.
Quantum Physics Mesoscale and Nanoscale Physics
STaDA: Style Transfer as Data Augmentation
The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data.
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes.
