VMamba introduces a state-space vision backbone using 2D selective scanning across four routes to achieve linear complexity and strong performance on image tasks.
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MMDetection: Open mmlab detection toolbox and benchmark
Tool reference. 88% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.
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abstract
We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The toolbox started from a codebase of MMDet team who won the detection track of COCO Challenge 2018. It gradually evolves into a unified platform that covers many popular detection methods and contemporary modules. It not only includes training and inference codes, but also provides weights for more than 200 network models. We believe this toolbox is by far the most complete detection toolbox. In this paper, we introduce the various features of this toolbox. In addition, we also conduct a benchmarking study on different methods, components, and their hyper-parameters. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. Code and models are available at https://github.com/open-mmlab/mmdetection. The project is under active development and we will keep this document updated.
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representative citing papers
Swin Transformer reaches 87.3% ImageNet accuracy and sets new records on COCO detection and ADE20K segmentation by replacing global self-attention with shifted-window local attention inside a hierarchical pyramid.
Text embeddings in MM-DiTs contain a detectable omission signal for missing concepts, and amplifying it via OSI reduces concept omission in generated images on FLUX.1-Dev and SD3.5-Medium.
Introduces the triplet segmentation task, CholecTriplet-Seg dataset with over 30,000 frames, and TargetFusionNet architecture extending Mask2Former for instance-level grounding of surgical <instrument, verb, target> triplets.
OD3 presents an optimization-free dataset distillation framework for object detection that reports new state-of-the-art accuracy on COCO and VOC at compression ratios from 0.25% to 5%.
FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
Auto-FlexSwitch achieves efficient dynamic model merging by decomposing task vectors into sparse masks, signs, and scalars, then making the compression learnable via gating and adaptive bit selection with KNN-based retrieval.
KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
UHR-DETR delivers 2.8% higher mAP and 10x faster inference than sliding-window baselines for small object detection in UHR remote sensing imagery on a single 24GB GPU.
FRTSearch reframes fast radio transient detection as instance segmentation on dynamic spectra and uses the segmented shapes to infer dispersion measure and time of arrival, achieving 98% recall with over 99.9% fewer false positives than traditional methods.
Deformba introduces context-adaptive state fusion to vision SSMs for better spatial augmentation and cross-stream interactions, showing strong results on 2D classification/detection/segmentation and 3D BEV perception benchmarks.
Clear2Fog generates realistic synthetic fog from clear scenes, enabling mixed-density training that outperforms full fixed-density data and improves real-world performance by 1.67 mAP after learning-rate adjustment.
Presents the first management-oriented multi-modal benchmark GovLA-10K and a vision-language reasoning framework GovLA-Reasoner with a spatially-aware adapter for low-altitude aerial perception.
NN-RAG extracts 1,289 candidate neural modules from 19 PyTorch repositories, validates 941 of them, and supplies roughly 72% of the novel structures in the LEMUR dataset while enabling cross-repository migration.
Mogao presents a causal unified model with deep fusion, dual encoders, and interleaved position embeddings that achieves strong performance on multi-modal understanding, text-to-image generation, and coherent interleaved outputs including zero-shot editing.
SPANetV2 is a vision backbone built around a new spectral-adaptive modulation mixer that outperforms prior models on ImageNet-1K classification, COCO detection, and ADE20K segmentation.
Introduces TimberVision dataset and multi-task framework for log-component segmentation, detection, and tracking in forestry operations using RGB images.
Four object detection models achieve over 90% average precision detecting excretions in pigsties from thermal images and remain reasonably robust on out-of-distribution data from different barns.
UniISP unifies ISP processing with a Hybrid Attention Module and Feature Adapter to produce images that are both visually pleasing for humans and informative for computer vision models.
HiPR improves 3D occupancy prediction by reparameterizing image-to-voxel projections using LiDAR-derived height priors to adapt sampling ranges to scene sparsity and height variations.
SignDATA provides a reproducible, config-driven preprocessing toolkit that converts heterogeneous sign language corpora into standardized pose or video outputs using interchangeable backends and privacy-aware options.
Granularity-aware distillation improves tree instance segmentation accuracy on real forest images by merging logits and unifying masks from fine-grained synthetic teachers despite coarse real labels.
DualEngage fuses transformer-encoded student motion dynamics with 3D scene features via softmax-gated fusion to recognize group engagement in classroom videos, reporting 96.21% average accuracy on a university dataset.
Telescope uses learnable hyperbolic foveation to deliver a 76% relative mAP gain (0.185 to 0.326) for objects beyond 250 meters while keeping overhead low.
citing papers explorer
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Swin Transformer reaches 87.3% ImageNet accuracy and sets new records on COCO detection and ADE20K segmentation by replacing global self-attention with shifted-window local attention inside a hierarchical pyramid.
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Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers
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Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach
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OD3: Optimization-free Dataset Distillation for Object Detection
OD3 presents an optimization-free dataset distillation framework for object detection that reports new state-of-the-art accuracy on COCO and VOC at compression ratios from 0.25% to 5%.
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FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometry
FractalMamba++ scales Vision Mamba across resolutions by using Hilbert fractal serialization, hierarchy-based skip connections, and fractal-aware 2D rotary position encoding.
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Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression
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KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition
KAConvNet introduces a Kolmogorov-Arnold Convolutional Layer to build networks competitive with ViTs and CNNs while offering stronger theoretical interpretability.
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UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
UHR-DETR delivers 2.8% higher mAP and 10x faster inference than sliding-window baselines for small object detection in UHR remote sensing imagery on a single 24GB GPU.
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FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation
FRTSearch reframes fast radio transient detection as instance segmentation on dynamic spectra and uses the segmented shapes to infer dispersion measure and time of arrival, achieving 98% recall with over 99.9% fewer false positives than traditional methods.
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Deformba: Vision State Space Model with Adaptive State Fusion
Deformba introduces context-adaptive state fusion to vision SSMs for better spatial augmentation and cross-stream interactions, showing strong results on 2D classification/detection/segmentation and 3D BEV perception benchmarks.
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A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
Clear2Fog generates realistic synthetic fog from clear scenes, enabling mixed-density training that outperforms full fixed-density data and improves real-world performance by 1.67 mAP after learning-rate adjustment.
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Focus on What Really Matters in Low-Altitude Governance: A Management-Centric Multi-Modal Benchmark with Implicitly Coordinated Vision-Language Reasoning Framework
Presents the first management-oriented multi-modal benchmark GovLA-10K and a vision-language reasoning framework GovLA-Reasoner with a spatially-aware adapter for low-altitude aerial perception.
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A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks
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Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
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Spectral-Adaptive Modulation Networks for Visual Perception
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TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations
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Excretion Detection in Pigsties Using Convolutional and Transformerbased Deep Neural Networks
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UniISP: A Unified ISP Framework for Both Human and Machine Vision
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Height-Guided Projection Reparameterization for Camera-LiDAR Occupancy
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SignDATA: Data Pipeline for Sign Language Translation
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Granularity-Aware Transfer for Tree Instance Segmentation in Synthetic and Real Forests
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Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
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Agentic Pipeline for Self-Synchronized Multiview Joint Angle Monitoring in Uncalibrated Environments
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COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection
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Common Inpainted Objects In-N-Out of Context
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Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
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A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation
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