Search Results for author:
Found 15 papers, 10 papers with code
End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention
Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world 3D hyperspectral images.
Ranked #7 on
Spectral Reconstruction
on Real HSI
MoDE: CLIP Data Experts via Clustering
The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data.
Supervised Masked Knowledge Distillation for Few-Shot Transformers
Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features.
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection
Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data.
TempCLR: Temporal Alignment Representation with Contrastive Learning
For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly.
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning.
Few-Shot Object Detection with Fully Cross-Transformer
Inspired by the recent work on vision transformers and vision-language transformers, we propose a novel Fully Cross-Transformer based model (FCT) for FSOD by incorporating cross-transformer into both the feature backbone and detection head.
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks
Few-shot object detection (FSOD) aims to detect never-seen objects using few examples.
Class incremental learning for video action classification
Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally.
Partner-Assisted Learning for Few-Shot Image Classification
In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection.
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
Deep Tensor ADMM-Net for Snapshot Compressive Imaging
In this paper, we propose a deep tensor ADMM-Net for video SCI systems that provides high-quality decoding in seconds.
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
In order to jointly capture the self-attention across multiple dimensions, including time, location and the sensor measurements, while maintain low computational complexity, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner.
