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Found 10 papers, 4 papers with code
GaTector: A Unified Framework for Gaze Object Prediction
In this paper, we build a novel framework named GaTector to tackle the gaze object prediction problem in a unified way.
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
Specifically, the ability of using mask prior to help detect objects is learned from base categories and transferred to novel categories.
Online Influence Maximization with Node-level Feedback Using Standard Offline Oracles
So we focus on how to use the standard offline influence maximization oracle which finds the best seed set given the edge parameters as input.
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue.
Ranked #1 on
Image Classification
on
Network Inference and Influence Maximization from Samples
Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks
Eddy current testing (ECT) is an effective technique in the evaluation of the depth of metal surface defects.
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs.
Optimization from Structured Samples for Coverage Functions
We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data.
Learning Compositional Neural Information Fusion for Human Parsing
The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively.
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Segmentation is a fundamental task in medical image analysis.
Ranked #1 on
Lung Nodule Segmentation
on LUNA
(mIoU metric)
