Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. It can be used to develop and evaluate object detectors in aerial images. Notably, the established COCO benchmark has Contact us on:hello@paperswithcode. COCO Captions contains over one and a half million captions describing over 330,000 images. 2. COCO-QA is a dataset for visual question answering. 4 types of questions: object, number, color, location. The main The COCO-MIG benchmark (Common Objects in Context Multi-Instance Generation) is a benchmark used to evaluate the generation capability of generators on text containing multiple attributes of multi-instance objects. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. In 2015 additional test set of 81K images was MSCOCO. The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. in Microsoft COCO Captions: Data Collection and Evaluation Server. 4 PQ on COCO), and semantic segmentation (60. We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting. We propose a novel hybrid Mamba-Transformer backbone, denoted as MambaVision, which is specifically tailored for vision applications. We quantify the speed versus quality trade-off of Paint Transformer: Feed Forward Neural Painting with Stroke Prediction. The current state-of-the-art on COCO panoptic is VAN-B6*. Close. See a full comparison of 1 papers with code. We achieve new state-of-the-art performance on COCO-WholeBody, significantly boosting the whole-body AP of RTMPose-l from 64. 31. 2021. Facial Landmark Detection is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. Sep 22, 2023 · We evaluate DE-ViT on few-shot, and one-shot object detection benchmarks with Pascal VOC, COCO, and LVIS. Provide: a high-level explanation of the dataset characteristics. There are totally 150 semantic categories, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Semi-Supervised Object Detection. in COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval. See a full comparison of 34 papers with code. Retrieval-Augmented Multimodal Language Modeling. in SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set. See a full comparison of 10 papers with code. The current state-of-the-art on COCO Captions is LeakGAN. Object detection on drone-captured scenarios is a recent popular task. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. TACO is a growing image dataset of waste in the wild. 2041 2020 Papers With Code is a free resource with all data licensed under CC-BY-SA. 2023. ×. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both Combining them results in DetectoRS, which significantly improves the performances of object detection. It contains 47,776 images (38,118 in train set and 9,658 in test set), 600 HOI categories constructed by 80 object categories and 117 verb classes. The end-to-end training gradually improves pseudo label qualities during the curriculum, and the more and more accurate pseudo labels in turn benefit object detection training. Text-to-Image Generation is a task in computer vision and natural language processing where the goal is to generate an image that corresponds to a given textual description. Introduced by Havard et al. Mar 29, 2016 · Edit social preview. e. Papers With Code highlights trending Machine Learning research and the code to implement it. The current state-of-the-art on COCO test-challenge is Simple Base+*. Edit social preview. **Object Detection** is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. PGI can provide complete input information for the target task to calculate objective function, so that reliable gradient information can be obtained to update network weights. Notably, Mask DINO establishes the best results to date on instance segmentation (54. Images should be at least 640×320px (1280×640px for best display). In 2015 additional test set of 81K images was Oct 9, 2022 · 327 papers with code • 11 benchmarks • 19 datasets. By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Introduced by Zhan et al. 18 Dec 2018 · Zhe Cao , Gines Hidalgo , Tomas Simon , Shih-En Wei , Yaser Sheikh ·. Splits: The first version of MS COCO dataset was released in 2014. Here are the key details about RefCOCO: Collection Method: The dataset was collected using the ReferitGame, a two-player game. Following the layout of the COCO dataset, each instance is assigned random color information, and MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes. For each image in V-COCO, we collect their corresponding captions from MS-COCO and automatically align the concept triplet in V-COCO to the tokens in the caption. com . COCO test-dev YOLOv7 (161 fps) box mAP Papers With Code is a free resource with all data licensed under CC-BY-SA. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. See a full comparison of 15 papers with code. The current state-of-the-art on MS COCO is YOLOv6-L6 (1280). Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. Code. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations. Jul 29, 2023 · Comprehensive experiments show the superiority of our proposed simple yet effective methods. 4 questions on average) per image 10 ground truth answers per question 3 plausible (but likely incorrect) answers per question Automatic evaluation metric The first version of the dataset was released in October 2015. , only 1% COCO labels. See a full comparison of 31 papers with code. DETRs with Collaborative Hybrid Assignments Training. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. Introduced by Ren et al. explain motivations and summary of its content. OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. The current state-of-the-art on MS COCO is RSN. The current state-of-the-art on COCO 2017 is MaxViT-B. 1404 papers with code • 29 benchmarks • 115 datasets. See a full comparison of 36 papers with code. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting. JHU-CLSP/NeoCoder • 12 Jul 2024 This is achieved by (1) Denial Prompting pushes LLMs to come up with more creative solutions to a given problem by incrementally imposing new constraints on the previous solution, compelling LLMs to adopt new strategies, and (2) defining and computing the NeoGauge metric which examines Separated COCO. See a full comparison of 5 papers with code. 41. 5 AP on COCO), panoptic segmentation (59. The platform and the underlying methodology allow to benchmark in the same It is the second version of the VQA dataset. The current state-of-the-art on COCO minival is OneFormer (InternImage-H, emb_dim=1024, single-scale). 47. Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection. The current state-of-the-art on V-COCO is RLIPv2. TACO. Photometrically Distorted Synthetic COCO (PDS-COCO) dataset is a synthetically created dataset for homography estimation learning. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called "beyond-boundary generation". COCO. 2022. TermsData policyCookies policyfrom. 2018. Occluded COCO. The annotations are provided in COCO format. 2014. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects. Source: Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. Introduced by Liang et al. 2% F-score @ 62FPS. U2Seg is also a strong pretrained model for few-shot segmentation, surpassing CutLER by +5. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. 8% to 66. 7. COCO-OOD dataset contains only unknown categories, consisting of 504 images with fine-grained annotations of 1655 unknown objects. 8 mIoU on ADE20K) among models under one billion parameters. The ADE20K semantic segmentation dataset contains more than 20K scene-centric images exhaustively annotated with pixel-level objects and object parts labels. 1. The current state-of-the-art on COCO-20i (1-shot) is PGMA-Net (ResNet-101). We present a new method that views object detection as a direct set prediction problem. Keypoint Detection is essential for analyzing and interpreting images in computer vision. This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The current state-of-the-art on Separated COCO is Swin-B + Cascade Mask R-CNN (tri-layer modelling). As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Zero-Shot Cross-Modal Retrieval. object removal, image restoration, manipulation, re-targeting, compositing, and image-based Text Generation. 5 days ago · MambaVision: A Hybrid Mamba-Transformer Vision Backbone. SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation. We evaluate fifty object detectors and find that models that predict visually sharper masks score higher on COCO-ReM, affirming that they were being incorrectly penalized due to errors in COCO-2017. See a full comparison of 16 papers with code. It forms a crucial part of vision recognition, alongside The current state-of-the-art on MS-COCO is ADDS(ViT-L-336, resolution 1344). in case of Human Pose Estimation. Papers With Code is a free resource with all data licensed under CC-BY-SA. See a full comparison of 110 papers with code. The current state-of-the-art on COCO 2014 is InternVL-G. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The current state-of-the-art on MS-COCO is SeeDS. Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. 0% PQ for panoptic segmentation. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 person instances. 2016. COCONut: Modernizing COCO Segmentation. 2 mAP on 30-shot and one-shot SoTA by 2. Add a new code entry for this paper. PDF Abstract. It exploits the usage scenario of code generation systems to make the original programming instruction more concrete by incorporating features known to be contained in the original code. V-COCO provides 10,346 images (2,533 for training, 2,867 for validating and 4,946 for testing) and 16,199 Associative Embedding: End-to-End Learning for Joint Detection and Grouping. Experiment results. 4 AP in 12 epochs and 51. The current state-of-the-art on MS COCO is OneFormer (InternImage-H, emb_dim=1024, single-scale). MCIC-COCO. See a full comparison of 8 papers with code. Relations in Captions (REC-COCO) is a new dataset that contains associations between caption tokens and bounding boxes in images. See a full comparison of 19 papers with code. See a full comparison of 77 papers with code. See a full comparison of 59 papers with code. Here we show some results of our model based on deep neural networks. The code is made publicly available. COCO-CN. COCO Captions. It builds on top of FUNIT by identifying the content loss problem and then addressing it with a novel content-conditioned style encoder architecture. We hope our simple yet effective method can inspire more research on unsupervised universal image segmentation. In this game, the first player views an image with a segmented target object and writes Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation. , multi-class, or binary) where each instance is only associated with a single class label. The goal is to train a model on a few examples of each object class and then use the model to detect objects in new images. Answers are all one-word. This benchmark consists of 800 sets of examples sampled from the COCO dataset. 5 0. See a full comparison of 27 papers with code. potential use cases of the dataset. HICO-DET is a dataset for detecting human-object interactions (HOI) in images. The current state-of-the-art on COCO-20i (5-shot) is SegGPT (ViT). Mar 27, 2024 · We develop COCO-ReM (Refined Masks), a cleaner set of annotations with visibly better mask quality than COCO-2017. The current state-of-the-art on COCO-Stuff test is EVA. 4 504 Billion 2019 Papers With Code is a free resource with all data licensed under CC-BY-SA. 6. Pose Flow: Efficient Online Pose Tracking. See a full comparison of 18 papers with code. Click to add a brief description of the dataset (Markdown and LaTeX enabled). On COCO test-dev, DetectoRS achieves state-of-the-art 55. The current state-of-the-art on COCO-Text is Corner-based Region Proposals. 93. GitHub, GitLab or BitBucket URL:*. COCO-OOD. See a full comparison of 206 papers with code. 🏆 SOTA for Instance Segmentation on COCO test-dev (AP50 metric) Papers With Code is a free resource with all data licensed under CC-BY-SA. The current state-of-the-art on MS-COCO (30-shot) is DE-ViT. 150. . The current state-of-the-art on MS COCO is OmniPose (WASPv2). The current state-of-the-art on COCO test-dev is Mask DINO (single scale). See a full comparison of 45 papers with code. **Panoptic Segmentation** is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. Submit. Contact us on:hello@paperswithcode. 78736 train questions. ADE20K. 22 papers with code. MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. The images are collected from different sensors and platforms. Aug 26, 2021 · Implemented in 2 code libraries. Close Save. Swin-T + Mask R-CNN. See a full comparison of 73 papers with code. 3% AP. It consists of: 123287 images. Panoptic Segmentation. SPEECH-COCO contains speech captions that are generated using text-to-speech (TTS) synthesis resulting in 616,767 spoken captions (more than 600h) paired with images. 2019. in Exploring Models and Data for Image Question Answering. Jan 30, 2023 · The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. tensorflow/models • • CVPR 2016 Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. We then showcase panorama generation within a cylindrical coordinate system Scene Text Detection. DE-ViT establishes new state-of-the-art results on all benchmarks. Homepage. It contains 164K images split into training (83K), validation (41K) and test (41K) sets. In 2015 additional test set of 81K images was 74. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. There are 164k images in COCO-stuff dataset that span over 172 categories including 80 things, 91 The current state-of-the-art on COCO test-dev is ViTPose (ViTAE-G, ensemble). The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. COCO-QA. in Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects. We introduce an efficient stuff annotation protocol based on superpixels, which leverages the original thing annotations. The FUNIT method suffers from the content loss problem—the The current state-of-the-art on COCO 2017 val is Salience-DETR (Focal-L 1x). More statistical analysis will be public after the acceptance of the paper. 8 AP50. ISDA: Position-Aware Instance Segmentation with Deformable Attention. The RefCOCO dataset is a referring expression generation (REG) dataset used for tasks related to understanding natural language expressions that refer to specific objects in images. Feature Weighting and Boosting for Few-Shot Segmentation. 37 code implementations in PyTorch, JAX and TensorFlow. 0 AP$^{\text{mask}}$ when trained on a low-data regime, e. 5% mask AP for instance segmentation, and 50. Few-Shot Object Detection is a computer vision task that involves detecting objects in images with limited training data. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. For the training and validation images, five independent human generated captions are be provided for each image. Paper. See a full comparison of 12 papers with code. Introduced by Li et al. CVPR 2024 · Xueqing Deng , Qihang Yu , Peng Wang , Xiaohui Shen , Liang-Chieh Chen ·. COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. The current state-of-the-art on COCO test-dev is EVA. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various This section with the source code will be public after the acceptance of the paper. S-COCO. See a full comparison of 38 papers with code. Source: SPEECH-COCO: 600k Visually Grounded Spoken COCO. 265,016 images (COCO and abstract scenes) At least 3 questions (5. Notably, for COCO, DE-ViT surpasses the few-shot SoTA by 15 mAP on 10-shot and 7. See a full comparison of 6 papers with code. See a full comparison of 22 papers with code. DINO achieves 49. May 10, 2021 · In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning. Additionally, their formulation allows for a guiding mechanism to control the image The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. There are two common metrics LSTD: A Low-Shot Transfer Detector for Object Detection. YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries. in Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual 89. The dataset consists of 328K images. In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Papers With Code is a free resource High-Resolution Image Synthesis with Latent Diffusion Models. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Enter. We can perform kernel space alignment Contact us on:hello@paperswithcode. This is an extension of single-label classification (i. The current state-of-the-art on COCO-WholeBody is DWPose. The current state-of-the-art on COCO 10% labeled data is MixPL. A large-scale machine comprehension dataset (based on the COCO images and captions). Dec 18, 2018 · OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. The current state-of-the-art on COCO minival is M3I Pre-training (InternImage-H). See all 30 tasks. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. The current state-of-the-art on MS-COCO (1-shot) is hANMCL. Occluded COCO is automatically generated subset of COCO val dataset, collecting partially occluded objects for a large variety of categories in real images in a scalable manner, where target object is partially occluded but the segmentation mask is DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method for anchor initialization, and a look forward twice scheme for box prediction. in A Tri-Layer Plugin to Improve Occluded Detection. Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity. Zero-Shot Object Detection. 11224 leaderboards • 5000 tasks • 10246 datasets • 135742 papers with code. YOLO. g. This requires finding the token for concepts such 158 papers with code • 7 benchmarks • 11 datasets. 38948 test questions. Introduced by Chen et al. 5%, even surpassing RTMPose-x teacher with 65. 3 AP in 24 epochs on COCO with a ResNet-50 backbone and multi-scale 403 papers with code • 10 benchmarks • 28 datasets. Official code from paper authors. Deep Residual Learning for Image Recognition. HICO-DET provides more than 150k annotated human-object pairs. These images are manually labelled and segmented according to a hierarchical taxonomy to train and evaluate object detection algorithms. The current state-of-the-art on MS COCO is BLIP-2 (ViT-G, fine-tuned). Seed0. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e. The current state-of-the-art on MS COCO is ExpansionNet v2. It contains images of litter taken under diverse environments: woods, roads and beaches. Jan 26, 2016 · This paper describes the COCO-Text dataset. DOTA is a large-scale dataset for object detection in aerial images. Feb 21, 2024 · We proposed the concept of programmable gradient information (PGI) to cope with the various changes required by deep networks to achieve multiple objectives. 94. 6% mAp, 98. Benchmarking Language Model Creativity: A Case Study on Code Generation. nvlabs/mambavision • • 10 Jul 2024. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. The unified network can generate a unified representation to simultaneously serve various tasks. The current state-of-the-art on MS COCO is 4xRSN-50(384×288). SPEECH-COCO. The idea is exactly the same as in the Synthetic COCO (S-COCO) dataset with SSD-like image distortion added at the beginning of the whole procedure: the first step involves adjusting the brightness of the 47 papers with code • 10 benchmarks • 16 datasets. All annotations consist of original annotations in COCO and the augmented annotations on the 286 papers with code • 12 benchmarks • 17 datasets Image Inpainting is a task of reconstructing missing regions in an image. The current state-of-the-art on COCO 2014 is VAST. We release a series of models with different sizes, from tiny to COCO-FUNIT is few-shot image translation model which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias. Separated COCO is automatically generated subsets of COCO val dataset, collecting separated objects for a large variety of categories in real images in a scalable manner, where target object segmentation mask is separated into distinct regions by the In this work, we propose a novel technique COCO to test the robustness of code generation systems. Keypoints, also known as interest points, are spatial locations or points in the image that define what is 80 papers with code • 8 benchmarks • 7 datasets. Our model achieves 98. Multi-Person Pose Estimation. 7% box AP for object detection, 48. The instances in DOTA Deep Visual-Semantic Alignments for Generating Image Descriptions. REC-COCO is based on the MS-COCO and V-COCO datasets. Introduced by Ding et al. It involves simultaneously detecting and localizing interesting points in an image. 9. hq ok fp vi iu ym vw hl bv jy