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Object Recognition

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하위 태스크: 3
평가표: 9
보존된 평가 기록: 31

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

<span style="color:grey; opacity: 0.6">( Image credit: Tensorflow Object Detection API )</span>

코드

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

https://github.com/leongatys/DeepTextures

공식 코드

Texture Synthesis Using Convolutional Neural Networks

https://github.com/limhyungtae/patchwork

공식 코드

Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor

https://github.com/liuzhuang13/DenseNet

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Densely Connected Convolutional Networks

https://github.com/lmb-freiburg/two-effects-one-trigger

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Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

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

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

https://github.com/lorenmt/vsl

공식 코드

Learning a Hierarchical Latent-Variable Model of 3D Shapes

https://github.com/louity/patches

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The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods

https://github.com/lqm26/bmcnet-esr

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Bilateral Event Mining and Complementary for Event Stream Super-Resolution

https://github.com/lqm26/rmfnet

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Efficient Event Stream Super-Resolution with Recursive Multi-Branch Fusion

https://github.com/lrp-ivc/stsbench

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STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving

https://github.com/lufanma/ifr

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Implicit Feature Refinement for Instance Segmentation

https://github.com/luizgh/adversarial_signatures

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Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification

https://github.com/lukasc-ch/ExtendedBitPlaneCompression

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EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

https://github.com/luodian/sf-moe-dg

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Sparse Mixture-of-Experts are Domain Generalizable Learners

https://github.com/lupantech/iconqa

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IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning

https://github.com/maciejczyzewski/neural-chessboard

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Chessboard and chess piece recognition with the support of neural networks

https://github.com/mala-lab/dfb

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Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features

https://github.com/marcosvendramini/GeMOS

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Opening Deep Neural Networks with Generative Models

https://github.com/margitantal68/featurelearning

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Feature Learning for Accelerometer based Gait Recognition

https://github.com/marionacaros/3d-object-segmentation

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Object Segmentation of Cluttered Airborne LiDAR Point Clouds

https://github.com/marleneberke/metacog

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MetaCOG: A Hierarchical Probabilistic Model for Learning Meta-Cognitive Visual Representations

https://github.com/megayeye/triangle-net

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Triangle-Net: Towards Robustness in Point Cloud Learning

https://github.com/memoatwit/universalperturbation

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One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation

https://github.com/mgb45/mkfbodytracker_pdaf

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Single camera pose estimation using Bayesian filtering and Kinect motion priors

https://github.com/microsoft/ORBIT-Dataset

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ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition

https://github.com/microsoft/metric-transfer.pytorch

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Deep Metric Transfer for Label Propagation with Limited Annotated Data

https://github.com/mikewangwzhl/paxion

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Paxion: Patching Action Knowledge in Video-Language Foundation Models

https://github.com/mindsetvision/mindset-vision

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MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

https://github.com/minkyu-choi04/rs-rnn

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Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noises

https://github.com/modeltc/united-perception

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The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition

https://github.com/morganbdt/crumb

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Tuned Compositional Feature Replays for Efficient Stream Learning

https://github.com/mrghofrani/lvlm-count

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LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models

https://github.com/msracver/Relation-Networks-for-Object-Detection

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Relation Networks for Object Detection

https://github.com/myfavouritekk/T-CNN

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T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

https://github.com/myuansun/eventrpg

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EventRPG: Event Data Augmentation with Relevance Propagation Guidance

https://github.com/naver-ai/dawin

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DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation

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