Search Results for author:
Found 22 papers, 13 papers with code
Analysis of Classifier-Free Guidance Weight Schedulers
Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models.
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild
In this paper, we propose FunnyNet-W, a model that relies on cross- and self-attention for visual, audio and text data to predict funny moments in videos.
Collaborating Foundation Models for Domain Generalized Semantic Segmentation
Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference.
Learning the What and How of Annotation in Video Object Segmentation
To reduce this annotation cost, in this paper, we propose EVA-VOS, a human-in-the-loop annotation framework for video object segmentation.
Reward Function Design for Crowd Simulation via Reinforcement Learning
Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner.
BluNF: Blueprint Neural Field
BluNF provides a robust and user-friendly 2D blueprint, enabling intuitive scene editing.
One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
Departing from the common notion of transferring only the target ``texture'' information, we leverage text-to-image diffusion models (e. g., Stable Diffusion) to generate a synthetic target dataset with photo-realistic images that not only faithfully depict the style of the target domain, but are also characterized by novel scenes in diverse contexts.
Data Augmentation
One-shot Unsupervised Domain Adaptation
+2
MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation
Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the limited available multi-modal data most efficiently.
Machine Learning for Brain Disorders: Transformers and Visual Transformers
For image classification, the most common Transformer Architecture uses only the Transformer Encoder in order to transform the various input tokens.
UGAE: A Novel Approach to Non-exponential Discounting
We also show experimentally that agents with non-exponential discounting trained via UGAE outperform variants trained with Monte Carlo advantage estimation.
SCAM! Transferring humans between images with Semantic Cross Attention Modulation
In this work, we introduce SCAM (Semantic Cross Attention Modulation), a system that encodes rich and diverse information in each semantic region of the image (including foreground and background), thus achieving precise generation with emphasis on fine details.
Ranked #1 on
Pose Transfer
on CelebAMask-HQ
Understanding reinforcement learned crowds
Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.
A Survey on Reinforcement Learning Methods in Character Animation
Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment.
Name Your Style: An Arbitrary Artist-aware Image Style Transfer
Moreover, it can mimic the styles of one or many artists to achieve attractive results, thus highlighting a promising direction in image style transfer.
Multiple Style Transfer via Variational AutoEncoder
Modern works on style transfer focus on transferring style from a single image.
Face, Body, Voice: Video Person-Clustering with Multiple Modalities
In this paper we make contributions to address both these deficiencies: first, we introduce a Multi-Modal High-Precision Clustering algorithm for person-clustering in videos using cues from several modalities (face, body, and voice).
LAEO-Net++: revisiting people Looking At Each Other in videos
For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos.
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods.
Ranked #4 on
Vehicle Re-Identification
on VehicleID Medium
LAEO-Net: revisiting people Looking At Each Other in videos
For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos.
Joint Learning of Object and Action Detectors
dog and cat jumping, enabling to detect actions of an object without training with these object-actions pairs.
Action Tubelet Detector for Spatio-Temporal Action Localization
We propose the ACtion Tubelet detector (ACT-detector) that takes as input a sequence of frames and outputs tubelets, i. e., sequences of bounding boxes with associated scores.
Spatio-Temporal Action Localization
Temporal Action Localization
Analysing domain shift factors between videos and images for object detection
Object detection is one of the most important challenges in computer vision.
