"Explaining Visual Biases as Words by Generating Captions. (arXiv:2301.11104v1 [cs.LG])" — Diagnosing the potential biases in image classifiers by leveraging two types (generative and discriminative) of pre-trained vision-language models to describe the visual bias as a word.

Paper: http://arxiv.org/abs/2301.11104

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Concept of the proposed bias-to…

"Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities. (arXiv:2301.11100v1 [cs.CV])" — An exploration of the extent to which zero-shot vision-language models exhibit gender bias for different vision tasks.

Paper: http://arxiv.org/abs/2301.11100

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(a) The average precision (AP) …

"simple diffusion: End-to-end diffusion for high resolution images. (arXiv:2301.11093v1 [cs.CV])" — Improve denoising diffusion for high resolution images while keeping the model as simple as possible and obtaining performance comparable to the latent diffusion-based approaches?

Paper: http://arxiv.org/abs/2301.11093

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A dslr photo of a frog wearing…

"Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning. (arXiv:2301.11063v1 [cs.CV])" — A method to reduce the parameters and FLOPs for computational efficiency in deep learning models by introducing accuracy and efficiency coefficients to control the trade-off between the accuracy of the network and its computing efficiency.

Paper: http://arxiv.org/abs/2301.11063

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Reward at varying Accuracies an…

"Explore the Power of Dropout on Few-shot Learning. (arXiv:2301.11015v1 [cs.CV])" — An exploration of the power of the dropout regularization technique on few-shot learning and provide some insights about how to use it.

Paper: http://arxiv.org/abs/2301.11015

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The generalization power of tra…

"On the Importance of Noise Scheduling for Diffusion Models. (arXiv:2301.10972v1 [cs.CV])" — A study of the effect of noise scheduling strategies for denoising diffusion generative models which finds that the noise scheduling is crucial for performance.

Paper: http://arxiv.org/abs/2301.10972

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Random samples generated by our…

"ITstyler: Image-optimized Text-based Style Transfer. (arXiv:2301.10916v1 [cs.CV])" — A data-efficient text-based style transfer method that does not require optimization at the inference stage where the text input is converted to the style space of the pre-trained VGG network to realize a more effective style swap.

Paper: http://arxiv.org/abs/2301.10916

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Overview stylization results of…

"Distilling Cognitive Backdoor Patterns within an Image. (arXiv:2301.10908v1 [cs.LG])" — A simple method to distill and detect backdoor patterns within an image by extracting the "minimal essence" from an input image responsible for the model's prediction.

Paper: http://arxiv.org/abs/2301.10908
Code: https://github.com/HanxunH/CognitiveDistillation

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On the left, First row: a clean…

"Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. (arXiv:2108.13161v7 [cs.CL] CROSS LISTED)" — A novel pluggable, extensible, and efficient large-scale pre-trained language model approach which can convert small language models into better few-shot learners without any prompt engineering.

Paper: http://arxiv.org/abs/2108.13161
Code: https://github.com/zjunlp/DART

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The architecture of DifferentiA…

"VN-Transformer: Rotation-Equivariant Attention for Vector Neurons. (arXiv:2206.04176v3 [cs.CV] UPDATED)" — A novel "VN-Transformer" architecture to address several shortcomings of the current Vector Neuron (VN) models.

Paper: http://arxiv.org/abs/2206.04176

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VN-Transformer (“early fusion”)…

"Towards Arbitrary Text-driven Image Manipulation via Space Alignment. (arXiv:2301.10670v1 [cs.CV])" — A new Text-driven image Manipulation framework via Space Alignment (TMSA) which aims to align the same semantic regions in CLIP and StyleGAN spaces so that the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description.

Paper: http://arxiv.org/abs/2301.10670

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Overview of image manipulation …

"Toward Realistic Evaluation of Deep Active Learning Algorithms in Image Classification. (arXiv:2301.10625v1 [cs.CV])" — An Active Learning (AL) benchmarking suite and a set of extensive experiments on five datasets shedding light on the questions: when and how to apply AL.

Paper: http://arxiv.org/abs/2301.10625

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The Active Learning loop featur…

"Variation-Aware Semantic Image Synthesis. (arXiv:2301.10551v1 [cs.CV])" — A new requirement for semantic image synthesis (SIS) to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation.

Paper: http://arxiv.org/abs/2301.10551

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Examples of class-level mode co…

"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…