Fahim Farook
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868I'm currently working on my second novel which is complete, but is in the edit stage. I wrote my first novel over 20 years ago but then didn't write much till now.
I post about #Coding, #Flutter, #Writing, #Movies and #TV. I'll also talk about #Technology, #Gadgets, #MachineLearning, #DeepLearning and a few other things as the fancy strikes ...
Lived in: ๐ฑ๐ฐ๐ธ๐ฆ๐บ๐ธ๐ณ๐ฟ๐ธ๐ฌ๐ฒ๐พ๐ฆ๐ช๐ซ๐ท๐ช๐ธ๐ต๐น๐ถ๐ฆ๐จ๐ฆ
Fahim Farook
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"Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning. (arXiv:2302.14794v1 [cs.CV])" โ Rather than using a frozen language model to communicate visual concepts, this method uses a meta -mapper to act as a bridge between large-scale visiona and language models.
Paper: http://arxiv.org/abs/2302.14794
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Multimodal few-shot meta-learniโฆ
Paper: http://arxiv.org/abs/2302.14794
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Multimodal few-shot meta-learniโฆ
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Fahim Farook
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"TextIR: A Simple Framework for Text-based Editable Image Restoration. (arXiv:2302.14736v1 [cs.CV])" โ Using text input to restore damaged images by specifying how to fill in the damage areas by way of text descriptions.
Paper: http://arxiv.org/abs/2302.14736
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Overview of image restoration rโฆ
Paper: http://arxiv.org/abs/2302.14736
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Overview of image restoration rโฆ
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Fahim Farook
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"Towards Enhanced Controllability of Diffusion Models. (arXiv:2302.14368v1 [cs.CV])" โ Creating a diffusion model that is easier to edit/style based on input images by conditioning the model on a spatial content mask and a flattened style embedding.
Paper: http://arxiv.org/abs/2302.14368
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Comparison of the proposed modeโฆ
Paper: http://arxiv.org/abs/2302.14368
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Comparison of the proposed modeโฆ
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Fahim Farook
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"One-Shot Video Inpainting. (arXiv:2302.14362v1 [cs.CV])" โ A method to inpaint videos where instead of having to provide masks for each frame, you only need to provide the object mask for the initial frame in the video sequence.
Paper: http://arxiv.org/abs/2302.14362
#AI #CV #NewPaper #DeepLearning #MachineLearning
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Qualitative comparison between โฆ
Paper: http://arxiv.org/abs/2302.14362
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Qualitative comparison between โฆ
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Fahim Farook
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"Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAM. (arXiv:2302.14354v1 [cs.CV])" โ Using machine learning to identify damage and defects to cultural heritage buildings using Convolutional Neural Networks (CNN).
Paper: http://arxiv.org/abs/2302.14354
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Comparing a picture with small-โฆ
Paper: http://arxiv.org/abs/2302.14354
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Comparing a picture with small-โฆ
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Fahim Farook
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"Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems. (arXiv:2301.01914v2 [cs.CV] UPDATED)" โ Comparing the accuracy and fidelity of images generated by DALL-E 2 and Luna, which is Stable Diffusion-based.
Paper: http://arxiv.org/abs/2301.01914
Luna code: https://github.com/slowy07/luna
#AI #CV #NewPaper #DeepLearning #MachineLearning
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Selected image samples from theโฆ
Paper: http://arxiv.org/abs/2301.01914
Luna code: https://github.com/slowy07/luna
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Selected image samples from theโฆ
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Fahim Farook
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"Diffusion Posterior Sampling for General Noisy Inverse Problems. (arXiv:2209.14687v3 [stat.ML] UPDATED)" โ Extending diffusion solvers to efficiently handle general noisy (non)linear inverse problems via approximation of the posterior sampling.
Paper: http://arxiv.org/abs/2209.14687
Code: https://github.com/dps2022/diffusion-posterior-sampling
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Solving noisy linear, and nonliโฆ
Paper: http://arxiv.org/abs/2209.14687
Code: https://github.com/dps2022/diffusion-posterior-sampling
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Solving noisy linear, and nonliโฆ
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Fahim Farook
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"Subspace Diffusion Generative Models. (arXiv:2205.01490v2 [cs.LG] UPDATED)" โ Restricting diffusion via projections onto subspaces to reduce computational time and cost without affecting the overall quality of the generated image.
Paper: http://arxiv.org/abs/2205.01490
Code: https://github.com/bjing2016/subspace-diffusion
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Random high resolution samples โฆ
Paper: http://arxiv.org/abs/2205.01490
Code: https://github.com/bjing2016/subspace-diffusion
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Random high resolution samples โฆ
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Fahim Farook
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"Large Scale Visual Food Recognition. (arXiv:2103.16107v3 [cs.CV] UPDATED)" โ A food dataset with 2,000 categories and over 1 million images that can be used for food recognition.
Paper: http://arxiv.org/abs/2103.16107
Code: https://github.com/Liuyuxinict/prenet/
#AI #CV #NewPaper #DeepLearning #MachineLearning
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The distributions over each catโฆ
Paper: http://arxiv.org/abs/2103.16107
Code: https://github.com/Liuyuxinict/prenet/
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The distributions over each catโฆ
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Fahim Farook
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"Directed Diffusion: Direct Control of Object Placement through Attention Guidance. (arXiv:2302.13153v1 [cs.CV])" โ Controlling object placement in diffusion models by way of attention guidance.
Paper: http://arxiv.org/abs/2302.13153
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Directed Diffusion (DD) key resโฆ
Paper: http://arxiv.org/abs/2302.13153
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Directed Diffusion (DD) key resโฆ
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Fahim Farook
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"In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages" โ Measuring the formality of the generated text for different languages using multilingual generative language models.
Paper: https://arxiv.org/abs/2302.12299
#AI #NewPaper #DeepLearning #MachineLearning #Language
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Differences between formal and โฆ
Paper: https://arxiv.org/abs/2302.12299
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Differences between formal and โฆ
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Fahim Farook
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"ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation. (arXiv:2209.04145v6 [cs.CV] UPDATED)" โ Using 2D images as a stepping stone for creating 3D shapes and eliminating the need for paired text-shape data.
Paper: http://arxiv.org/abs/2209.04145
Code: https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
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Our novel โImage as Stepping Stโฆ
Paper: http://arxiv.org/abs/2209.04145
Code: https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation
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Our novel โImage as Stepping Stโฆ
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Fahim Farook
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"Modulating Pretrained Diffusion Models for Multimodal Image Synthesis. (arXiv:2302.12764v1 [cs.CV])" โ Multimodal Conditioning Modules (MCM) for enabling conditional image synthesis using pretrained diffusion models so that you can generate images using not just a text prompt, but additional input such as a segmentation map or a sketch.
Paper: http://arxiv.org/abs/2302.12764
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Multimodal conditioning modulesโฆ
Paper: http://arxiv.org/abs/2302.12764
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Multimodal conditioning modulesโฆ
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Fahim Farook
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"Surface Recognition for e-Scooter Using Smartphone IMU Sensor. (arXiv:2302.12720v1 [eess.SP])" โ Detecting whether an e-scooter is on a paved road or a sidewalk using the Inertial Measurement Unit (IMU) sensors on a smartphone.
Paper: http://arxiv.org/abs/2302.12720
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Left: an example of a street wiโฆ
Paper: http://arxiv.org/abs/2302.12720
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Left: an example of a street wiโฆ
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Fahim Farook
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"Data-driven Approach for Automatically Correcting Faulty Road Maps. (arXiv:2211.06544v2 [cs.CV] UPDATED)" โ A method to fix faulty road maps (specifically roads displayed on maps) using machine learning.
Paper: http://arxiv.org/abs/2211.06544
Code: https://github.com/soojunghong/image_inpainting_model_for_lane_geomery_discovery
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GT, Input, and Output denotes tโฆ
Paper: http://arxiv.org/abs/2211.06544
Code: https://github.com/soojunghong/image_inpainting_model_for_lane_geomery_discovery
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GT, Input, and Output denotes tโฆ
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Fahim Farook
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"ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors. (arXiv:2210.05950v2 [cs.CV] UPDATED)" โ Better image inpainting by detecting structures in the source image using techniques such as edge detection.
Paper: http://arxiv.org/abs/2210.05950
Code: https://github.com/dqiaole/zits_inpainting
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Left (a)-(e): Comparisons of ZIโฆ
Paper: http://arxiv.org/abs/2210.05950
Code: https://github.com/dqiaole/zits_inpainting
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Left (a)-(e): Comparisons of ZIโฆ
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Fahim Farook
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"Designing an Encoder for Fast Personalization of Text-to-Image Models. (arXiv:2302.12228v1 [cs.CV])" โ A method to teach text-to-image models new concepts in seconds.
Paper: http://arxiv.org/abs/2302.12228
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Our encoder-based method enableโฆ
Paper: http://arxiv.org/abs/2302.12228
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Our encoder-based method enableโฆ
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Fahim Farook
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"Aligning Text-to-Image Models using Human Feedback. (arXiv:2302.12192v1 [cs.LG])" โ A fine-tuning method for better aligning generated images to the input text prompt when using diffusion models.
Paper: http://arxiv.org/abs/2302.12192
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The steps in our fine-tuning meโฆ
Paper: http://arxiv.org/abs/2302.12192
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The steps in our fine-tuning meโฆ
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Fahim Farook
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"Evaluating the Efficacy of Skincare Product: A Realistic Short-Term Facial Pore Simulation. (arXiv:2302.11950v1 [cs.CV])" โ Simulating the effects of skincare products on your skin (specifically the pores) to gauge efficacy of the product.
Paper: http://arxiv.org/abs/2302.11950
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Qualitative results of Facial Pโฆ
Paper: http://arxiv.org/abs/2302.11950
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Qualitative results of Facial Pโฆ
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Fahim Farook
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"Region-Aware Diffusion for Zero-shot Text-driven Image Editing. (arXiv:2302.11797v1 [cs.CV])" โ A region-aware text-guided image editing method which aims to replace one entity with another.
What I always wonder with these approaches is whether you can replace a larger entity with a smaller one, or vice versa, (say a horse with a cat) in a way that looks realistic?
Paper: http://arxiv.org/abs/2302.11797
Code: https://github.com/haha-lisa/RDM-Region-Aware-Diffusion-Model
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The results of the proposed regโฆ
What I always wonder with these approaches is whether you can replace a larger entity with a smaller one, or vice versa, (say a horse with a cat) in a way that looks realistic?
Paper: http://arxiv.org/abs/2302.11797
Code: https://github.com/haha-lisa/RDM-Region-Aware-Diffusion-Model
#AI #CV #NewPaper #DeepLearning #MachineLearning
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The results of the proposed regโฆ
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