Papers with Code 아카이브

2D Object Detection

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전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/lizixing23/AMBOKD

공식 코드

Adaptive Modality Balanced Online Knowledge Distillation for Brain-Eye-Computer based Dim Object Detection

https://github.com/lizzy8587/castdet

공식 코드

Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning

https://github.com/ljacksonpan/ratrack

공식 코드

RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud

https://github.com/lkeab/BCNet

공식 코드

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers

https://github.com/lktidaohuoxing/superedge

공식 코드

SuperEdge: Towards a Generalization Model for Self-Supervised Edge Detection

https://github.com/lliuz/mixteacher

공식 코드

MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection

https://github.com/lluckyyh/vt-ada

공식 코드

Vision Transformer-based Adversarial Domain Adaptation

https://github.com/lluisgomez/TextTopicNet

공식 코드

TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces

https://github.com/lmb-freiburg/locov

공식 코드

Localized Vision-Language Matching for Open-vocabulary Object Detection

https://github.com/lmb-freiburg/ovqa

공식 코드

Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy

https://github.com/lmmh058/fsatfusion

공식 코드

FSATFusion: Frequency-Spatial Attention Transformer for Infrared and Visible Image Fusion

https://github.com/lmmmeng/overlock

공식 코드

OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels

https://github.com/lmmmeng/sparx

공식 코드

SparX: A Sparse Cross-Layer Connection Mechanism for Hierarchical Vision Mamba and Transformer Networks

https://github.com/lmmmeng/transxnet

공식 코드

TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition

https://github.com/logsanand/Monocular-depth-UAVimages

공식 코드

Self-supervised monocular depth estimation from oblique UAV videos

https://github.com/loiccordone/object-detection-with-spiking-neural-networks

공식 코드

Object Detection with Spiking Neural Networks on Automotive Event Data

https://github.com/lojzezust/pansr

공식 코드

PanSR: An Object-Centric Mask Transformer for Panoptic Segmentation

https://github.com/lolipopshock/Detectron2_AL

공식 코드

OLALA: Object-Level Active Learning for Efficient Document Layout Annotation

https://github.com/long-username/DTDN-DTDN-Dual-task-De-raining-Network

공식 코드

DTDN: Dual-task De-raining Network

https://github.com/longbai1006/surgical-vqla

공식 코드

Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery

https://github.com/longchencv/uodreview

공식 코드

Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future

https://github.com/longkukuhi/armbench

공식 코드

RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models

https://github.com/lookdeep/ai-norms-2024

공식 코드

Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings

https://github.com/looooongchen/sortedap

공식 코드

SortedAP: Rethinking evaluation metrics for instance segmentation

https://github.com/lorebianchi98/fg-clip

공식 코드

Is CLIP the main roadblock for fine-grained open-world perception?

https://github.com/lorebianchi98/fg-ovd

공식 코드

The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding

https://github.com/lovelyqian/CDFSOD-benchmark

공식 코드

Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector

https://github.com/lovelyqian/ntire2025_cdfsod

공식 코드

NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results

https://github.com/lowlevelai/glare

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GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval

https://github.com/ltzovo/damamba

공식 코드

DAMamba: Vision State Space Model with Dynamic Adaptive Scan

https://github.com/luckybird1994/hqsod

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Disentangled High Quality Salient Object Detection

https://github.com/luckybird1994/mmcpf

공식 코드

Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection

https://github.com/luckybird1994/samcod

공식 코드

Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection

https://github.com/lugan113/TXL-PBC_Dataset

공식 코드

TXL-PBC: a freely accessible labeled peripheral blood cell dataset

https://github.com/luigisigillo/shipinsight

공식 코드

Ship in Sight: Diffusion Models for Ship-Image Super Resolution

https://github.com/luisfelipewb/potato

공식 코드

PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.