"DEJA VU: Continual Model Generalization For Unseen Domains. (arXiv:2301.10418v1 [cs.LG])" — A framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.

Paper: http://arxiv.org/abs/2301.10418

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Overview of applying our framew…

"Local Feature Extraction from Salient Regions by Feature Map Transformation. (arXiv:2301.10413v1 [cs.CV])" — A framework that robustly extracts and describes salient local features regardless of changing light and viewpoints by suppressing illumination variations and encouraging structural information to ignore the noise from light and to focus on edges.

Paper: http://arxiv.org/abs/2301.10413

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Proposed framework. Network con…

"A Fast Feature Point Matching Algorithm Based on IMU Sensor. (arXiv:2301.10293v1 [cs.CV])" — An algorithm using the inertial measurement unit (IMU) to optimize the efficiency of image feature point matching, in order to reduce the time consumed when matching feature points in simultaneous localization and mapping (SLAM).

Paper: http://arxiv.org/abs/2301.10293

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Two adjacent frames. The left i…

Yesterday's #StableDiffusion prompt was: "Unseen Academicals".

The results were the most diverse and furthest away from anything to do with the original #DiscWorld title that I've gotten so far 🙂 But on the other hand, the prompt is rather abstract ...

I am reaching the end of the DiscWorld title list and it might be time to take a break from DiscWorld and try something else for a few days?

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Stable Diffusion prompt: "Unsee…
Stable Diffusion prompt: "Unsee…
Stable Diffusion prompt: "Unsee…
Stable Diffusion prompt: "Unsee…

"Intelligent Painter: Picture Composition With Resampling Diffusion Model. (arXiv:2210.17106v2 [cs.CV] UPDATED)" — An intelligent painter that generate a person's imaginary scene in one go, given explicit hints as to the objects to appear in the final image, their placement in the image etc.

Paper: http://arxiv.org/abs/2210.17106
Code: https://github.com/vinesmsuic/ipainter-diffusion

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The architecture of the propose…

"Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels. (arXiv:2210.10605v3 [cs.CV] UPDATED)" — A plug & play framework based on linearized ADMM that allows you to bypass the computation of intractable proximal operators.

Paper: http://arxiv.org/abs/2210.10605
Code: https://github.com/claroche-r/pnp_ladmm

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Example of sample from the test…

"Self-Supervised Learning Through Efference Copies. (arXiv:2210.09224v2 [cs.LG] UPDATED)" — A paper which shows that the commonly used self-supervised learning approach of transforming each training datapoint into a pair of views, using the knowledge of this pairing as a positive (i.e. non-contrastive) self-supervisory sign, and potentially opposing it to unrelated, (i.e. contrastive) negative examples is an incomplete implementation of a concept from neuroscience, the Efference Copy (EC).

Paper: http://arxiv.org/abs/2210.09224

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Efference copy (EC). A) Sensory…

"Continual Transformers: Redundancy-Free Attention for Online Inference. (arXiv:2201.06268v3 [cs.AI] UPDATED)" — A novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input stream.

Paper: http://arxiv.org/abs/2201.06268
Code: https://github.com/lukashedegaard/continual-transformers

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Multi-block Continual Transform…

"Model soups to increase inference without increasing compute time. (arXiv:2301.10092v1 [cs.CV])" — A comparison of Model Soups performances on three different models (ResNet, ViT and EfficientNet) using three Soup Recipes (Greedy Soup Sorted, Greedy Soup Random and Uniform soup) and a new Soup Recipe called Pruned Soup with better performance.

Paper: http://arxiv.org/abs/2301.10092
Code: https://github.com/milo-sobral/modelsoup

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A table showing the accuracies …

"Side Eye: Characterizing the Limits of POV Acoustic Eavesdropping from Smartphone Cameras with Rolling Shutters and Movable Lenses. (arXiv:2301.10056v1 [cs.CR])" — A paper discussing the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras.

Paper: http://arxiv.org/abs/2301.10056

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Illustration of the POV optical…

"Progressive Meta-Pooling Learning for Lightweight Image Classification Model. (arXiv:2301.10038v1 [cs.CV])" — A framework to make the receptive field learnable for a lightweight network, unlike the conventional efficient learning methods which ignore the role of the receptive field in neural network design, by using parameterized pooling-based operations.

Paper: http://arxiv.org/abs/2301.10038

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Overview of the proposed Meta-P…

"Transfer Learning for Olfactory Object Detection. (arXiv:2301.09906v1 [cs.CV])" — An investigation of the effect of style and category similarity in multiple datasets used for object detection pretraining. The paper discusses using object recognition as a basis for the indirect recognition of more complex and meaningful "smell" references such as gestures and spaces.

Note: Similar to another paper, the word "olfactory" in the title confused me. Is "olfactory" being used in a slightly different context in machine learning?

Paper: http://arxiv.org/abs/2301.09906

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Smell. The Five Senses. 1558 – …

"ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition. (arXiv:2301.09878v1 [cs.CV])" — The challenge aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heritage. It appears that they concentrate on smell-relevant objects rather than using smell to detect objects ...

Note: The use of the word "olfactory" is a bit confusing, in my opinion, in this paper and one other which came out today.

Paper: http://arxiv.org/abs/2301.09878

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Example image from the challeng…

Yesterday's #StableDiffusion prompt was: "Making Money".

Not any of the resulting images had anything to do with the #DiscWorld novel, and some didn't even seem to fit the concept of the title, but hey, that's how it goes, right? 😛

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Stable Diffusion prompt: "Makin…
Stable Diffusion prompt: "Makin…
Stable Diffusion prompt: "Makin…
Stable Diffusion prompt: "Makin…

"The Infinite Index: Information Retrieval on Generative Text-To-Image Models. (arXiv:2212.07476v2 [cs.IR] UPDATED)" — A reframing of prompt engineering for generative models as interactive text-based retrieval on a novel kind of "infinite index".

Paper: http://arxiv.org/abs/2212.07476

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Overview of indexing approaches…

"Look at Adjacent Frames: Video Anomaly Detection without Offline Training. (arXiv:2207.13798v5 [cs.CV] UPDATED)" — A solution to detect anomalous events in videos without the need to train a model offline, based on a randomly-initialized multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information.

Paper: http://arxiv.org/abs/2207.13798

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Two figures, the first show the…

"Synthesis of Compositional Animations from Textual Descriptions. (arXiv:2103.14675v6 [cs.CV] UPDATED)" — A technique for generating compositional actions, which handles complex input sentences and outputs a 3D pose sequence depicting the actions in the input sentence.

Paper: http://arxiv.org/abs/2103.14675
Code: https://github.com/anindita127/complextext2animation

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Overview of our proposed method…

"Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff. (arXiv:2301.09522v1 [cs.CV])" — A proposal to strengthen the marriage between SNNs and event-based inputs by considering anytime optimal inference SNNs, or AOI-SNNs, which can terminate anytime during the inference to achieve optimal inference result.

Paper: http://arxiv.org/abs/2301.09522
Code: https://github.com/Dengyu-Wu/SNN-Regularisation-Cutoff

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An illustrative diagram showing…

"OvarNet: Towards Open-vocabulary Object Attribute Recognition. (arXiv:2301.09506v1 [cs.CV])" — An examination of the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.

Paper: http://arxiv.org/abs/2301.09506

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The first row depicts the tasks…

"RainDiffusion:When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining. (arXiv:2301.09430v1 [cs.CV])" — An unsupervised image deraining paradigm based on diffusion models which introduces stable training of unpaired real-world data instead of weak adversarial training.

Paper: http://arxiv.org/abs/2301.09430

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Image deraining results on a re…