"Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training. (arXiv:2210.07688v2 [cs.CL] UPDATED)" — A study of the object hallucination problem in large-scale Vision-Language Pre-trained (VLP) models from multiple aspects.

Paper: http://arxiv.org/abs/2210.07688
Code: No code in linked repo

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Comparison of image captioning …

"Dive into Deep Learning. (arXiv:2106.11342v4 [cs.LG] UPDATED)" — An open-source book on Deep Learning based on Jupyter Notebooks so that it contains interactive examples. Freely available and well-worth checking out.

Paper: http://arxiv.org/abs/2106.11342
Code: https://github.com/d2l-ai/d2l-en
Book: https://d2l.ai/

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Image of book cover for Dive in…

"Scaling Vision Transformers to 22 Billion Parameters. (arXiv:2302.05442v1 [cs.CV])" — A recipe for highly efficient and stable training of a 22B-parameter Vision Transformers (ViT) overtaking the previously known 4B parameter model.

Paper: http://arxiv.org/abs/2302.05442

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Dense prediction from frozen Vi…

"Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning. (arXiv:2302.05289v1 [cs.CV])" — A set of advanced image features that are inspired from the field of image quality assessment, to assess message veracIty in social networks, which exploits all message features by exploring various machine learning models.

Paper: http://arxiv.org/abs/2302.05289

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An overview of the proposed rum…

"Archaeological Sites Detection with a Human-AI Collaboration Workflow. (arXiv:2302.05286v1 [cs.CV])" — Using pre-trained semantic segmentation deep learning models to detect archaeological sites within the Mesopotamian floodplains environment.

Paper: http://arxiv.org/abs/2302.05286
Code: https://github.com/mister-magpie/tell_segmentation

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Investigation area. Orange dots…

"CEN-HDR: Computationally Efficient neural Network for real-time High Dynamic Range imaging. (arXiv:2302.05213v1 [cs.CV])" — A new computationally efficient neural network based on a light attention mechanism and sub-pixel convolution operations for real-time HDR imaging.

Paper: http://arxiv.org/abs/2302.05213
Code: https://github.com/steven-tel/CEN-HDR

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Qualitative comparison of the p…

"DOMINO: Domain-aware Loss for Deep Learning Calibration. (arXiv:2302.05142v1 [cs.CV])" — A domain-aware loss function to calibrate deep learning models so as to avoid the potential dangers of uncalibrated models in medical imaging.

Paper: http://arxiv.org/abs/2302.05142
Code: https://github.com/lab-smile/DOMINO

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Confusion matrices on testing s…

"Example-Based Sampling with Diffusion Models. (arXiv:2302.05116v1 [cs.GR])" — A generic way to produce 2-d point sets imitating existing samplers from observed point sets using a diffusion model which addresses the problem of convolutional layers by leveraging neighborhood information from an optimal transport matching to a uniform grid, that allows benefiting from fast convolutions on grids, and to support the example-based learning of non-uniform sampling patterns.

Paper: http://arxiv.org/abs/2302.05116

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Learning Rank-1 realizations us…

The images show two separate iterations of the same app — the first is a SwiftUI app for doing #StableDiffusion image generation using #CoreML. That took several weeks of work to create and it never really worked right — I could only do single selection of images, couldn’t drag and drop more than one image, and it took a fair amount of time to implement even trivial stuff.

The second is the same app implemented using AppKit. It took essentially one day (yesterday). Multiselection was built-in, drag and drop (for multiple items) was a couple of lines of code, and I implemented a bunch of other things I wanted to do fairly easily.

On top of that, the app feels faster, lighter, and more responsive than the SwiftUI version for the exact same task, using the exact same models.

SwiftUI has a long way to go still, if it will even ever get there …

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Screenshot of an image generati…
Screenshot of an image generati…

Opera is going to use #ChatGPT to shorten articles — so that people have to read less and think even less —, Microsoft is building ChatGPT into their Office tools — again, less thinking —, we already have ChatGPT in search engines ..

So this is how the world ends — everybody growing stupider because we are too lazy to think for ourselves, and not with a bang?

I guess SkyNet did arrive but not in the way we thought … 😛

#TheAIRevolution #MisinformationEngine
Engadget article — Opera is add…

For those of us outside the Dole/Chiquita hegemony, bananas can have a variety of tastes — sweet, sour, bland and all the other stuff in between 😛 So when you say “water bugs taste like banana”, which banana are we talking about?

https://en.wikipedia.org/wiki/Banana
Chefs who cook with insects rep…

I really miss having @qikipedia posting facts over here. For a week or so, they did and then they’ve gone back to only posting on Twitter 😞

So I guess I’ll have to repeat their facts since I do enjoy them … but then there is no quoting posts and I have to resort to screenshots .. Ah, well 🙂
There is a termite city underne…

"UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models. (arXiv:2302.04867v1 [cs.LG])" — A unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct.

Paper: http://arxiv.org/abs/2302.04867
Code: https://github.com/wl-zhao/UniPC

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The main idea of UniPC. We prov…

"Trading Information between Latents in Hierarchical Variational Autoencoders. (arXiv:2302.04855v1 [stat.ML])" — A generalization of VAEs to application domains beyond generative modeling (e.g., representation learning, clustering, or lossy data compression) by introducing an objective function that allows practitioners to trade off between the information content ("bit rate") of the latent representation and the distortion of reconstructed data.

Paper: http://arxiv.org/abs/2302.04855

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Left: trade-off between perform…

"Robot Synesthesia: A Sound and Emotion Guided AI Painter. (arXiv:2302.04850v1 [cs.CV])" — An approach for using sound and speech to guide a robotic painting process by encoding the simulated paintings and input sounds into the same latent space.

Paper: http://arxiv.org/abs/2302.04850
Code: https://github.com/pschaldenbrand/Frida

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Our robot painting sound guidan…

"Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation. (arXiv:2302.04841v1 [cs.CV])" — A study of the training dynamics of textual inversion, with the aim of speeding it up.

Paper: http://arxiv.org/abs/2302.04841

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A summary of our key findings: …

"Better Diffusion Models Further Improve Adversarial Training. (arXiv:2302.04638v1 [cs.CV])" — Tries to answer the question "Can better diffusion models further improve adversarial training?" empirically.

Paper: http://arxiv.org/abs/2302.04638
Code: https://github.com/wzekai99/DM-Improves-AT

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A brief summary comparison of t…

"Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. (arXiv:2302.04578v1 [cs.CV])" — Exploration into creating an image that is similar to another image for human vision, but unrecognizable for diffusion models to investigate the possibility of creating images that hinder diffusion models from extracting their features.

Paper: http://arxiv.org/abs/2302.04578

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Comparison of workflows for adv…

"Efficient Attention via Control Variates. (arXiv:2302.04542v1 [cs.LG])" — A look at control variates to show that Random-Feature-based Attention (RFA) can be decomposed into a sum of multiple control variate estimators for each element in the sequence.

Paper: http://arxiv.org/abs/2302.04542

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Left and middle: empirical memo…

"Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples. (arXiv:2302.04440v1 [cs.LG])" — A parametric sample-based score that uses density estimation to quantitatively measure the quality/diversity of generated samples of deep generative models while taking overfitting into account.

Paper: http://arxiv.org/abs/2302.04440
Code: https://github.com/marcojira/fls

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FLS for a generative model that…