代码拉取完成，页面将自动刷新
Computational Imaging is a the process of indirectly forming images from measurements using algorithms that rely on a significant amount of computing. In contrast to traditional imaging, computational imaging systems involve a tight integration of the sensing system and the computation in order to form the images of interest. The ubiquitous availability of fast computing platforms (such as multi-core CPUs and GPUs), the advances in algorithms and modern sensing hardware is resulting in imaging systems with significantly enhanced capabilities. Computational Imaging systems cover a broad range of applications include computational microscopy,[1] tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc. The integration of the sensing and the computation in computational imaging systems allows for accessing information which was otherwise not possible.
https://twitter.com/jerryjliu0/status/1734598198759759952 https://github.com/.../que.../query_transform_cookbook.ipynb
현재 모델에는 Neck 부분에서 정보를 성공적으로 통합할 수 없다는 문제가 있습니다. 특히 서로 다른 '계층'의 정보를 통합할 때 정보 손실이 발생하는 문제가 있다. 이 문제를 해결하는 새로운 방법으로 "Gather-and-Distribute"(GD) 메커니즘이 제안되었습니다. 이 메커니즘은 서로 다른 계층의 정보를 전반적으로 수집하고 높은 계층에 분산함으로써 정보 통합을 효율적으로 실현합니다. 이것은 넥 부분의 성능을 크게 향상시킵니다. 새로운 물체 검출 아키텍처인 Gold-YOLO는 이 GD 메커니즘을 도입하여 보다 높은 정밀도의 물체 검출을 실현하고 있습니다. 또한 Gold-YOLO는 ImageNet에서 사전 학습을 통해 모델 수렴 속도와 정확도를 크게 향상시킵니다. 기존의 YOLO 모델과 비교해도 Gold-YOLO는 현저한 정밀도를 자랑합니다. 구체적으로 Gold-YOLO-S는 선행 연구의 YOLOv6-3.0-S보다 높은 AP(Average Precision)를 달성했습니다.
DreamLLM: Synergistic Multimodal Comprehension and Creation : https://dreamllm.github.io/, Xi'an Jiaotong University외 "DreamLLM"이라는 새로운 기술은 다른 유사한 기술과 달리 이미지와 텍스트를 그대로 (원시 데이터) 형태로 입력으로 받아 동일한 형식으로 출력합니다. 예를 들어, 유명한 회화 그림과 '이 그림 설명'이라는 지시를 입력하면 DreamLLM은 정확한 설명을 생성할 수 있습니다. 많은 AI 모델은 데이터(특히 이미지)를 처리할 때 이를 일부 중간 표현으로 변환합니다. 그러나 DreamLLM은 이러한 중간 표현을 만드는 대신 데이터를 원형으로 처리합니다. 이 접근법의 장점은 모델이 데이터의 본질을 보다 직접적으로 파악하고 보다 정밀한 결과를 출력할 가능성이 높다는 점입니다. 게다가 DreamLLM은 인터넷 기사 등에서 텍스트와 이미지가 복잡하게 결합되어 있는 데이터라도 잘 이해하고 새로운 데이터를 유사한 형식으로 생성할 수 있습니다. 일반적으로 이러한 복잡한 데이터를 분석하고 생성하는 것은 어렵지만 DreamLLM은 'token'이라는 마크를 사용하여 텍스트에서 이미지가 어디에 배치되어야 하는지 예측합니다. DreamLLM은 텍스트와 이미지의 이해 능력으로 높은 평가를 받았습니다. 구체적으로는 MMBench와 MM-Vet이라는 텍스트와 이미지의 조합을 평가하는 테스트로 고득점을 획득하고, MS-COCO라는 이미지 생성의 정확도를 측정하는 테스트에서도 낮은 에러율을 기록하고 있습니다.
메타 논문. 수식 포함된 PDF -> LaTex/Markdown 문서 변환, 과학 문서를 마크업 언어로 처리하기 위해 광학 문자 인식(OCR) 작업을 수행하는 시각적 변환기 모델. 모델 및 코드도 공개 프로젝트 : https://facebookresearch.github.io/nougat/ 논문 : https://arxiv.org/abs/2308.13418 코드 : https://github.com/facebookresearch/nougat 데모 : https://huggingface.co/spaces/ysharma/nougat (내용:번역) 과학 지식은 주로 책과 과학 저널에 저장되며, 종종 PDF 형식으로 저장됩니다. 그러나 PDF 형식은 특히 수학적 표현의 경우 의미 정보가 손실되는 문제가 있습니다. 본 논문에서는 과학 문서를 마크업 언어로 처리하기 위해 광학 문자 인식(OCR) 작업을 수행하는 시각적 변환기 모델인 Nougat(학술 문서를 위한 신경 광학 이해)를 제안하고, 새로운 과학 문서 데이터 세트에 대한 모델의 효과를 입증합니다. 제안된 접근 방식은 사람이 읽을 수 있는 문서와 기계가 읽을 수 있는 텍스트 사이의 격차를 해소함으로써 디지털 시대에 과학 지식의 접근성을 향상시킬 수 있는 유망한 솔루션을 제공합니다. 과학 텍스트 인식에 대한 향후 작업을 가속화하기 위해 모델과 코드를 공개합니다.
A Novel Frame-Wise Video Representation Using Frame-Wise Flow Maps And Multi-Resolution Temporal Grids Paper: https://arxiv.org/pdf/2212.12294.pdf Github: https://github.com/maincold2/FFNeRV Project: https://maincold2.github.io/ffnerv/
Paper: https://arxiv.org/pdf/2203.13250.pdf Github: https://github.com/xingyizhou/GTR
Paper Summary: https://www.marktechpost.com/.../a-new-study-from-cmu.../ Paper: https://arxiv.org/pdf/2201.09792v1.pdf Github: https://github.com/locuslab/convmixer
github: https://github.com/rr8shah/TSARA datasets: https://zenodo.org/record/4526657 comparing models:
CUB-200-2011과 NABirds에서 92.3%와 92.7%를 달성하는 미세한 인식을 위한 통합된 메타 프레임워크 빠른 읽기: https://www.marktechpost.com/.../researchers-from.../ 종이: https://arxiv.org/pdf/2203.02751v1.pdf Github: https://github.com/dqshuai/metaformer
Quick Read: https://www.marktechpost.com/.../in-a-latest-computer.../ Project: https://waymo.com/research/block-nerf/ Paper: https://arxiv.org/pdf/2202.05263.pdf
Project link: https://infinite-nature.github.io/ Code: https://github.com/google-research/go... Colab demo: https://colab.research.google.com/git...
https://github.com/robmarkcole/satellite-image-deep-learning
Project webpage: https://sites.google.com/site/yihsuantsai/research/cvpr17-harmonization Contact: Yi-Hsuan Tsai (wasidennis at gmail dot com) https://github.com/wasidennis/DeepHarmonization
Quick Read: https://www.marktechpost.com/.../in-a-latest-computer.../ Project: https://waymo.com/research/block-nerf/ Paper: https://arxiv.org/pdf/2202.05263.pdf
Researchers From Tel Aviv University, UC Berkeley and NVIDIA Introduce ‘DETReg’, A Novel Unsupervised AI For Object Detection Quick Read: https://www.marktechpost.com/.../researchers-from-tel.../ Codes: https://github.com/amirbar/DETReg Project: https://www.amirbar.net/detreg/ Paper: https://arxiv.org/pdf/2106.04550.pdf
a new semantic segmentation method has been proposed. You can read about it in the link below: https://arxiv.org/abs/2105.15203 The details for training and testing the model for semantic segmentation using SegFormer are available at my GitHub Repository. https://github.com/.../Sematic_Segmentation_With_SegFormer
CCV'18: Deep High Dynamic Range Imaging with Large Foreground Motions Deep High Dynamic Range Imaging with Large Foreground Motions, Shangzhe Wu, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang, in ECCV, 2018. More results can be found on our project page. https://github.com/elliottwu/DeepHDR
Yuanming Hu1,2, Hao He1,2, Chenxi Xu1,3, Baoyuan Wang1, Stephen Lin1
End-to-End Object Detection with Transformers (ECCV 2020)
Searching for MobileNetV3 (ICCV 2019)
Deep Residual Learning for Image Recognition (CVPR 2016)
Image Style Transfer Using Convolutional Neural Networks (CVPR 2016)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (NIPS 2015)
소설 프레임워크로 이미지넷 평가를 멀티 레이블 작업으로 돌릴 수 있습니다. 페이퍼: https://arxiv.org/pdf/2101.05022.pdf Github: https://github.com/naver-ai/relabel_imagenet
Paper: https://arxiv.org/pdf/2011.02523.pdf Codes: https://github.com/apple/ml-hypersim
종이: http://geometrylearning.com/paper/DeepFaceDrawing.pdf 비디오: https://www.youtube.com/watch?v=HSunooUTwKs
Quick Read: https://www.marktechpost.com/.../mit-researchers-propose.../ Paper: https://arxiv.org/abs/2110.15352 Project: https://mcunet.mit.edu/
논문 요약: https://www.marktechpost.com/.../researchers-from-the.../ 페이퍼: https://arxiv.org/pdf/2112.03221.pdf GitHub: https://github.com/threedle/text2mesh 프로젝트 페이지: https://threedle.github.io/text2mesh/
Quick Read: https://www.marktechpost.com/.../meta-ai-and-cmu.../ Paper: https://arxiv.org/pdf/2112.12761.pdf Project: https://banmo-www.github.io/
" Current state-of-the-art GANs have seen immense progress, but they commonly operate in 2D and do not explicitly model the underlying 3D scenes. Recent work on 3D-aware GANs has begun to tackle the problem of multi-view-consistent image synthesis and, to a lesser extent, extraction of 3D shapes without being supervised on geometry or multi-view image collections. However, the image quality and resolution of existing 3D GANs have lagged far behind those of 2D GANs. One of the primary reasons for this gap is the computational inefficiency of previously employed 3D generators and neural rendering architectures. The authors of this paper introduce a novel generator architecture for unsupervised 3D representation learning from a collection of single-view 2D photographs that seeks to improve the computational efficiency of rendering while remaining true to 3D-grounded neural rendering. For this purpose, the authors introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real-time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, their framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN2, and inherit their efficiency and expressiveness."
Quick Read: https://www.marktechpost.com/.../researchers-at-meta-and.../ Paper: https://arxiv.org/pdf/2201.02605v2.pdf Github: https://github.com/facebookresearch/Detic
Quick Read: https://www.marktechpost.com/.../apple-ml-researchers.../ Paper: https://arxiv.org/pdf/2111.08897.pdf Github: https://github.com/apple/ARKitScenes
Quick Read: https://www.marktechpost.com/.../researchers-from-china.../ Paper: https://arxiv.org/pdf/2112.14000v1.pdf Github: https://github.com/BR-IDL/PaddleViT
Quick Read: https://www.marktechpost.com/.../meta-ai-and-cmu.../ Paper: https://arxiv.org/pdf/2112.12761.pdf Project: https://banmo-www.github.io/
깃허브 링크: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
Quick Read: https://www.marktechpost.com/.../researchers-from-nvidia.../ Paper: https://arxiv.org/pdf/2201.01266v1.pdf Github: https://github.com/.../research.../tree/master/SwinUNETR
젯슨나노2GB에 단안카메라로 제스쳐 모양에 따라 드론이 스탠드 후버링은 물론 좌/우로 제어가 되네요!
Paper: https://arxiv.org/pdf/2104.13369.pdf Project: https://explaining-in-style.github.io/ Github: https://github.com/google/explaining-in-style
A Single Vision (Computer Vision) Model For Many Different Visual Modalities Quick Read: https://www.marktechpost.com/2022/01/30/meta-ai-research-proposes-omnivore-a-single-vision-computer-vision-model-for-many-different-visual-modalities/ Paper: https://arxiv.org/abs/2201.08377 Github: https://github.com/facebookresearch/omnivore
You can read about it in the link below: https://arxiv.org/abs/2105.15203 I have prepared a python script which can be used to train the SegFormer model or alternatively you can download the model that I have trained and use it for the inference. The details for training and testing the model for semantic segmentation using SegFormer are available at my GitHub Repository. https://github.com/.../Sematic_Segmentation_With_SegFormer The sample inference from a test image is shown below:
자율주행에 대한 온전한 인식을 제공하기 위한 최신 컴퓨터 비전 연구에서 연구자들은 DSN, LiDAR 기반 판옵틱 세그먼트의 과제를 다루고 있다 빠른 읽기: https://www.marktechpost.com/.../in-a-latest-computer.../ 종이: https://arxiv.org/pdf/2203.07186v1.pdf Github: https://github.com/hongfz16/DS-Net
Images with a single camera can generate 3d model. https://kotai2003-faces.streamlit.app/
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification. In FPVT, Face Spatial Reduction Attention (FSRA) and Dimensionality Reduction (FDR) layers are employed to make the feature maps compact, thus reducing the computations. An Improved Patch Embedding (IPE) algorithm is proposed to exploit the benefits of CNNs in ViTs (e.g., shared weights, local context, and receptive fields) to model lower-level edges to higher-level semantic primitives. Within FPVT framework, a Convolutional Feed-Forward Network (CFFN) is proposed that extracts locality information to learn low level facial information. The proposed FPVT is evaluated on seven benchmark datasets and compared with ten existing state-of-the-art methods, including CNNs, pure ViTs, and Convolutional ViTs. Despite fewer parameters, FPVT has demonstrated excellent performance over the compared methods. I am greatly thankful to my coauthors Arif Mahmood and Zaigham Zaheer for their supervision and guidance throughout the project.
Paper: https://storage.googleapis.com/.../perception_test_report... Github link: https://github.com/deepmind/perception_test
Paper: https://arxiv.org/pdf/2209.10510.pdf Demo: http://imaginaire.cc/Lumos/ Project: https://deepimagination.cc/Lumos/
Paper: https://arxiv.org/pdf/2202.04901.pdf Github: https://github.com/google-research/frame-interpolation Project: https://film-net.github.io/
Paper: https://arxiv.org/pdf/2209.11224v2.pdf Github: https://github.com/williamyang1991/vtoonify
Paper: https://arxiv.org/pdf/2209.09019.pdf Github link: https://github.com/salesforce/LAVIS
Paper: https://arxiv.org/pdf/2101.04061v2.pdf Github link: https://github.com/TencentARC/GFPGAN
A Technique for Fine-Tuning a Text-to-Image Model with a very Limited Set of Images Paper: https://arxiv.org/pdf/2208.12242.pdf? Project: https://dreambooth.github.io/
Paper: https://openaccess.thecvf.com/.../Wu_CvT_Introducing... GIthub: https://github.com/microsoft/CvT
Paper: https://arxiv.org/pdf/2208.10442.pdf Github: https://github.com/microsoft/unilm/tree/master/beit
A Novel Computer Vision Framework that can Generate Neural Human Radiance Field from a Single Video Paper: https://arxiv.org/pdf/2203.12575v1.pdf Github: https://github.com/apple/ml-neuman
Paper: https://arxiv.org/pdf/2208.13040v1.pdf Github link: https://github.com/alibaba/EasyCV
A General-Purpose AI Framework For Image Modelling And Computer Vision Tasks Based On Probabilistic Frame Prediction Paper: https://arxiv.org/pdf/2203.09494.pdf
Paper: https://nihalsid.github.io/texturify/static/Texturify.pdf Project: https://nihalsid.github.io/texturify/
Paper: https://arxiv.org/pdf/2208.02816v1.pdf Github: https://github.com/microsoft/VideoX/tree/master/X-CLIP
Paper: https://arxiv.org/pdf/2111.09452.pdf GItHub link: https://arxiv.org/pdf/2111.09452.pdf
Paper: https://arxiv.org/pdf/2208.04309v1.pdf Github link: https://github.com/lahoud/3d-vision-transformers
Paper: https://arxiv.org/pdf/2205.14879v1.pdf Github link: https://github.com/kartikgill/easter2
Paper: https://arxiv.org/pdf/2207.11243v1.pdf Github link: https://github.com/facebookresearch/multiface
They propose a novel vision transformer termed CA-ViT that can fully utilize both global and local picture context dependencies while outperforming its predecessors by a wide margin. They introduce a unique HDR-Transformer that can reduce processing costs, ghosting artifacts, and recreating high-quality HDR photos. This is the first Transformer-based HDR de-ghosting framework to be developed. They undertake in-depth tests on three sample benchmark HDR datasets to compare HDR-performance Transformers to current state-of-the-art techniques. Paper: https://arxiv.org/pdf/2208.05114v1.pdf Github link: https://github.com/megvii-research/HDR-Transformer
Abstract: Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines.
Source: https://openaccess.thecvf.com/.../Kim_DiffusionCLIP_Text... Slides: https://www.slideshare.net/.../diffusionclip-textguided... Video: https://youtu.be/YVCtaXw6fw8 Code: https://github.com/gwang-kim/DiffusionCLIP.git
A Minimal Video Instance Segmentation (VIS) Framework That Achieves SOTA Performance With Neither Video-Based Architectures Nor Training Procedures Paper: https://arxiv.org/pdf/2208.02245v1.pdf Github link: https://github.com/nvlabs/minvis
Paper: https://openaccess.thecvf.com/.../Xia_Vision_Transformer... Github: https://github.com/LeapLabTHU/DAT
Paper: https://arxiv.org/pdf/2204.02389.pdf Github: https://github.com/rhgao/ObjectFolder Project: https://ai.stanford.edu/~rhgao/objectfolder2.0/
https://github.com/Harry-KIT/Image-Classification-on-small-datasets-in-Pytorch
A Novel Image Translation Architecture For Few-Shot Portrait Stylization Paper: https://arxiv.org/pdf/2207.02426v1.pdf Project: https://menyifang.github.io/projects/DCTNet/DCTNet.html Github link: https://github.com/menyifang/dct-net
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors 공헌자(저자): Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao 논문: https://arxiv.org/abs/2207.02696 GitHub: https://github.com/wongkinyiu/yolov7
초록: YOLOv7은 5FPS~160FPS 범위에서 속도와 정확도 모두에서 알려진 모든 객체 감지기를 능가하며 GPU V100에서 30FPS 이상의 알려진 모든 실시간 객체 감지기 중 가장 높은 정확도 56.8% AP를 가지고 있습니다. YOLOv7-E6 물체 감지기(56 FPS V100, 55.9% AP)는 변압기 기반 감지기인 SWIN-L Cascade-Mask R-CNN(9.2 FPS A100, 53.9% AP)보다 속도 509%, 정확도 2%, 컨볼루션 기반 검출기 ConvNeXt-XL Cascade-Mask R-CNN(8.6 FPS A100, 55.2% AP)은 속도 551%, AP 정확도 0.7% 향상 및 YOLOv7 성능 향상: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B 및 기타 여러 물체 감지기의 속도와 정확도. 또한 다른 데이터 세트나 사전 훈련된 가중치를 사용하지 않고 처음부터 MS COCO 데이터 세트에서만 YOLOv7을 훈련합니다.
Diffusion Models Beat GANs on Image Synthesis 확산 모델에 classifier를 추가해 다양성-품질 trade-off를 달성 Classifier-Free Diffusion Guidance Classifier 없이 단일 확산 모델로 같은 목표 달성 Cascaded Diffusion Models for High Fidelity Image Generation 여러 해상도의 확산 모델이 포함된 계층적 cascading pipeline으로 이전보다 더 높은 해상도에서 고품질 샘플 생성 Pretraining is All You Need for Image-to-Image Translation (PITI) 사전 훈련된 확산 모델을 이용해 다양한 다운스트림 작업 입력 조건(e.g. semantic map + text)에서 Image-to-Image translation 수행
다이나믹 배치를 구현하고 있고 충분히 작은 모델이라면 10000 ~ 20000 requests / sec 수준의 응답 성능을 낼 수 있습니다. 구현에 사용된 akka는 프레임워크가 아닌 고도의 동시성, 병렬성, 분산성을 가지고 있는 메세지 기반 어플레케이션 구축 툴킷으로 간주할 수 있습니다. 충분히 생산성이 있는 언어를 베이스로 하고 있기 때문에 직접 서빙 데몬에서 monolithic 한 구조로 비지니스 코드를 내재화 하는것도 가능합니다. 예제의 코드량이 적고 추상화가 거의 없는 naive한 구현이기 때문에 동작에 관련한 거의 대부분의 요소를 블랙박스 없이 확인하고 동시에 환경 튜닝이 가능합니다. 실제 예제의 사용성은 웹과 상호 작용을 하는 어플리케이션 보다는 검색, 추천, 대화 시스템등 다수의 모델을 컨트롤하는 기반 플랫폼 시스템에 적합합니다. https://github.com/go-noah/akka-dynamic-batch-serving/tree/main/akka-dynamic-batch-onnx-gpu-bert https://github.com/go-noah/akka-dynamic-batch-serving/tree/main/akka-dynamic-batch-tensorflow-gpu-bert
The First Neural Model To Execute Various AI Tasks Spanning Classical Computer Vision, Image Synthesis, Vision-and-Language, and Natural Language Processing NLP Demo: https://unified-io.allenai.org/
Paper: https://arxiv.org/pdf/2206.00272v1.pdf Github: https://github.com/huawei-noah/Efficient-AI-Backbones
EG3D, that can generate random images of faces and other objects with high resolution together with underlying geometric structures [Quick Read: https://www.marktechpost.com/2022/07/04/researchers-at-stanford-have-developed-an-artificial-intelligence-ai-model-eg3d-that-can-generate-random-images-of-faces-and-other-objects-with-high-resolution-together-with-underlying-geometric-s/?fbclid=IwAR3s59QXgJsrYG0uIiDTIIQl784LAUe48NrfJ6Vk6kTVVOjjHAzod7DRAEc Paper: https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.pdf?fbclid=IwAR2oL0AvGr_0uBamWB67pHl_KNSAuhxN2VKpyzLcpGiIBVIyJiy7211j_8M Github: https://github.com/NVlabs/eg3d
Paper: https://arxiv.org/pdf/2206.01720.pdf Project: https://stanfordvl.github.io/atp-revisit-video-lang/
Paper: https://arxiv.org/pdf/2112.10752.pdf Github: https://github.com/CompVis/latent-diffusion
Arxiv https://arxiv.org/abs/2112.11010 Code https://github.com/youngwanLEE/MPViT
code : https://github.com/CChenLi/Dynamic_Gender_Classification
code: https://github.com/NVlabs/eg3d paper: https://arxiv.org/abs/2112.07945 page: https://nvlabs.github.io/eg3d/ youtube: https://youtu.be/cXxEwI7QbKg
This framework can be used for pre-training models to achieve state-of-the-art performance on tasks such as video-text retrieval and question answering. Paper: https://arxiv.org/pdf/2112.09583.pdf Github: https://github.com/salesforce/alpro
Kindly Like and Share and subscribe to the YT channel !! Project Code: https://github.com/Ashishkumar-hub/Warehouse-Apparel-Detection-using...
(Self-Supervised Transformer With Energy-Based Graph Optimization): A Novel AI Framework That Distills Unsupervised Features Into High-Quality Discrete Semantic Labels Paper: https://arxiv.org/pdf/2203.08414.pdf Github: https://github.com/mhamilton723/STEGO
A Novel Synthetic Training Data Called Self-Blended Images (SBIs) To Detect Deepfakes Paper: https://arxiv.org/pdf/2204.08376.pdf Github: https://github.com/mapooon/SelfBlendedImages
A Deep Generative Technique Based On An Autoregressive Transformer For Text-To-Image Synthesis With Human Priors Paper Summary: https://www.marktechpost.com/.../meta-ai-introduces-make.../ Paper: https://arxiv.org/pdf/2203.13131v1.pdf
A New Self-Supervised Deep Learning Generative Approach Based On CLIP And VQ-GAN To Generate Reliable Samples From Text Prompts Paper: https://arxiv.org/pdf/2203.00386v1.pdf
Kindly Like and Share and subscribe to the YT channel !! Project Code: https://github.com/.../Warehouse-Apparel-Detection-using...
https://arxiv.org/abs/2104.07300 github: https://github.com/hongsukchoi/3DCrowdNet_RELEASE
An Object-Centric Video Model Trained To Predict Depth Signals From A Slot-Based Video Representation Paper: https://arxiv.org/pdf/2206.07764.pdf Project: https://slot-attention-video.github.io/savi++/
A Single Vision (Computer Vision) Model For Many Different Visual Modalities Paper: https://arxiv.org/abs/2201.08377 Github: https://github.com/facebookresearch/omnivore
GitHub: https://github.com/.../Iguana-detection-on-Nvidia-Jetson... 블로그 링크: https://blogs.nvidia.com.tw/.../green-iguana-detection.../
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