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Found 21 papers, 8 papers with code
Bring Metric Functions into Diffusion Models
The first module, similar to a standard DDPM, learns to predict the added noise and is unaffected by the metric function.
Video Understanding with Large Language Models: A Survey
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation
We hope our proposed framework, benchmark, and LMM evaluation could help establish the intriguing interleaved image-text generation task.
Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation
In this work, we use text-guided latent diffusion models for zero-shot image-to-image translation (I2I) across large domain gaps (longI2I), where large amounts of new visual features and new geometry need to be generated to enter the target domain.
Domain-Scalable Unpaired Image Translation via Latent Space Anchoring
However, given a UNIT model trained on certain domains, it is difficult for current methods to incorporate new domains because they often need to train the full model on both existing and new domains.
Online Causation Monitoring of Signal Temporal Logic
The classic STL monitoring is performed by computing a robustness interval that specifies, at each instant, how far the monitored signals are from violating and satisfying the specification.
Learning to Evaluate the Artness of AI-generated Images
Assessing the artness of AI-generated images continues to be a challenge within the realm of image generation.
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation
In contrast, we propose a parameter-free temporal shift module that can leverage the spatial U-Net as is for video generation.
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style.
Improving Visual-textual Sentiment Analysis by Fusing Expert Features
Visual-textual sentiment analysis aims to predict sentiment with the input of a pair of image and text.
Make-A-Video: Text-to-Video Generation without Text-Video Data
We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V).
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation
In particular, this method breaks through the limitations of the existing methods, not only achieves good results in multivariate separation, but also effectively separates signals when mixed with 40dB Gaussian noise signals.
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way.
Global Image Sentiment Transfer
This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer sentiments of images according to a given sentiment tag.
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
Extracting effective deep features to represent content and style information is the key to universal style transfer.
Ultrafast Photorealistic Style Transfer via Neural Architecture Search
Our method consists of a construction step (C-step) to build a photorealistic stylization network and a pruning step (P-step) for acceleration.
Learning One-Clock Timed Automata
We present an algorithm for active learning of deterministic timed automata with a single clock.
Formal Languages and Automata Theory
Fast Universal Style Transfer for Artistic and Photorealistic Rendering
Given a pair of images as the source of content and the reference of style, existing solutions usually first train an auto-encoder (AE) to reconstruct the image using deep features and then embeds pre-defined style transfer modules into the AE reconstruction procedure to transfer the style of the reconstructed image through modifying the deep features.
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks
Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying around 500x inference time speed-up.
NIL: Learning Nonlinear Interpolants
Nonlinear interpolants have been shown useful for the verification of programs and hybrid systems in contexts of theorem proving, model checking, abstract interpretation, etc.
Pyramid Attention Network for Semantic Segmentation
A Pyramid Attention Network(PAN) is proposed to exploit the impact of global contextual information in semantic segmentation.
