Search Results for author:
Found 16 papers, 8 papers with code
Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts
We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class.
Ranked #1 on
Few-Shot Semantic Segmentation
on COCO-20i (2-way 1-shot)
(using extra training data)
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
We propose to train detectors using only pristine images injecting in part of them crafted frequency patterns, simulating the effects of various deepfake generation techniques without being specific to any.
LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language
By leveraging an open science philosophy, this study contributes to Language Adaptation strategies for the Italian language by introducing the novel LLaMAntino family of Italian LLMs.
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
We evaluate the effectiveness of this approach by training a semantic segmentation model on a real dataset augmented in two ways: 1) using synthetic images obtained from real masks, and 2) generating images from synthetic semantic masks.
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
In recent years, deep learning has been successfully applied in various scientific domains.
Inductive Attention for Video Action Anticipation
Consequently, existing solutions based on the action recognition models are only suboptimal.
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds.
NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge.
Unified Recurrence Modeling for Video Action Anticipation
To this end, we propose a unified recurrence modeling for video action anticipation via message passing framework.
Efficient yet Competitive Speech Translation: FBK@IWSLT2022
The primary goal of this FBK's systems submission to the IWSLT 2022 offline and simultaneous speech translation tasks is to reduce model training costs without sacrificing translation quality.
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets
This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources, containing both human-annotated and web-collected captions.
From Show to Tell: A Survey on Deep Learning-based Image Captioning
Starting from 2015 the task has generally been addressed with pipelines composed of a visual encoder and a language model for text generation.
Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation
Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly.
Higher Order Recurrent Space-Time Transformer for Video Action Prediction
Endowing visual agents with predictive capability is a key step towards video intelligence at scale.
