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Found 21 papers, 15 papers with code
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research.
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing.
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control.
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Furthermore, the performance of SoftPatch and SoftPatch+ is comparable to that of the noise-free methods in conventional unsupervised AD setting.
MobileMamba: Lightweight Multi-Receptive Visual Mamba Network
CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios.
LLaVA-KD: A Framework of Distilling Multimodal Large Language Models
The success of Large Language Models (LLM) has led researchers to explore Multimodal Large Language Models (MLLM) for unified visual and linguistic understanding.
A Survey on Benchmarks of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning.
LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
In this paper, we propose a Large Language-and-Vision Assistant for Visual Spatial Description, named LLaVA-VSD, which is designed for the classification, description, and open-ended description of visual spatial relationships.
PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision
Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsistent data distributions.
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework that is highly extensible for new methods.
Efficient Multimodal Large Language Models: A Survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Ranked #5 on
Multi-class Anomaly Detection
on MVTec AD
DMAD: Dual Memory Bank for Real-World Anomaly Detection
To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Finally, we report the results of popular IAD methods on the Real-IAD dataset, providing a highly challenging benchmark to promote the development of the IAD field.
Transavs: End-To-End Audio-Visual Segmentation With Transformer
Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) Objects of the same category tend to produce similar audio signals, making it difficult to distinguish between them and thus leading to unclear segmentation results.
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case.
Calibrated Teacher for Sparsely Annotated Object Detection
Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations.
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images.
Ranked #49 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Learning Distinctive Margin toward Active Domain Adaptation
Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data.
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation
Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.
ISDNet: Integrating Shallow and Deep Networks for Efficient Ultra-High Resolution Segmentation
The huge burden of computation and memory are two obstacles in ultra-high resolution image segmentation.
