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Found 13 papers, 9 papers with code
Guiding Instruction-based Image Editing via Multimodal Large Language Models
Extensive experimental results demonstrate that expressive instructions are crucial to instruction-based image editing, and our MGIE can lead to a notable improvement in automatic metrics and human evaluation while maintaining competitive inference efficiency.
YMIR: A Rapid Data-centric Development Platform for Vision Applications
This paper introduces an open source platform to support the rapid development of computer vision applications at scale.
Implementation of an Automated Learning System for Non-experts
This paper detailed the engineering system implementation of an automated machine learning system called YMIR, which completely relies on graphical interface to interact with users.
ALBench: A Framework for Evaluating Active Learning in Object Detection
To facilitate the research in this field, this paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection.
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
Experiment results show that the proposed ParC-Net achieves better performance than popular light-weight ConvNets and vision transformer based models in common vision tasks and datasets, while having fewer parameters and faster inference speed.
Ranked #748 on
Image Classification
on ImageNet
Universal Object Detection with Large Vision Model
In this study, our focus is on a specific challenge: the large-scale, multi-domain universal object detection problem, which contributes to the broader goal of achieving a universal vision system.
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer
Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows.
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction
In this paper, we propose a neural architecture representation model that can be used to estimate these attributes holistically.
ParCNetV2: Oversized Kernel with Enhanced Attention
Specifically, we propose a new convolutional neural network, ParCNetV2, that extends position-aware circular convolution (ParCNet) with oversized convolutions and bifurcate gate units to enhance attention.
Learning Inhomogeneous FRAME Models for Object Patterns
We investigate an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns.
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models
Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary.
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search
Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy.
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs
Transformer models have made tremendous progress in various fields in recent years.
