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Found 35 papers, 11 papers with code
PVUW 2025 Challenge Report: Advances in Pixel-level Understanding of Complex Videos in the Wild
This report provides a comprehensive overview of the 4th Pixel-level Video Understanding in the Wild (PVUW) Challenge, held in conjunction with CVPR 2025.
STSeg-Complex Video Object Segmentation: The 1st Solution for 4th PVUW MOSE Challenge
Through finetuning the models and employing the Adaptive Pseudo-labels Guided Model Refinement Pipeline in the inference phase, the STSeg solution achieved a J&F score of 87. 26% on the test set of the 2025 4th PVUW Challenge MOSE Track, securing the 1st place and advancing the technology for video object segmentation in complex scenarios.
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings
HPTR enables the integration of radio signal features with expert knowledge, while FAF improves the modeling of high-frequency features critical for precise signal processing.
Learning Evolution via Optimization Knowledge Adaptation
Evolutionary algorithms (EAs) maintain populations through evolutionary operators to discover diverse solutions for complex tasks while gathering valuable knowledge, such as historical population data and fitness evaluations.
Knowledge-aware Evolutionary Graph Neural Architecture Search
According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset.
Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
In order to break through the limitations of the previous methods, we decouple the implied complex periodic variations into inclusion and overlap relationships among different level periodic components based on the observation of the multi-periodicity therein and its inclusion relationships.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise.
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification
Furthermore, we propose a multi-view signal (MVS) method to further improve the performance of hard-to-classify categories with few-shot trusted label samples.
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
Recent advancements in distilling 2D vision-language foundation models into neural fields, like NeRF and 3DGS, enable open-vocabulary segmentation of 3D scenes from 2D multi-view images without the need for precise 3D annotations.
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Automatic Graph Topology-Aware Transformer
Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities.
Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing
In the second stage, we shift focus on learning the appearance features of the source video.
Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification
Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence.
Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification
Besides, this work has implications for how to efficiently utilize the multi-features of PolSAR data to learn better high-level representation in CL and how to construct networks suitable for PolSAR data better.
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
In this work, we propose to leverage the geometric information of the feature distribution of the well-represented head class to guide the model to learn the underlying distribution of the tail class.
When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text.
Data-Centric Long-Tailed Image Recognition
In the context of the long-tail scenario, models exhibit a strong demand for high-quality data.
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference.
Changes-Aware Transformer: Learning Generalized Changes Representation
After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection.
AMC-Net: An Effective Network for Automatic Modulation Classification
Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems.
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes.
Ranked #19 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution.
Delving into Semantic Scale Imbalance
Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations.
PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening
Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network.
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Three image tasks and two video tasks of computer vision are investigated.
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
Second, the group spatial attention and group spectral attention modules are proposed to extract image features.
New Generation Deep Learning For Video Object Dection:A Survery
Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used.
C-CNN: Contourlet Convolutional Neural Networks
Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture.
A Survey of Deep Learning-based Object Detection
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity.
