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Found 16 papers, 6 papers with code
NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery
To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery.
Text-guided 3D Human Motion Generation with Keyframe-based Parallel Skip Transformer
Text-driven human motion generation is an emerging task in animation and humanoid robot design.
MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond
Addressing this gap, we introduce a new dataset in this domain, i. e. Multi-modality dataset for crop biomass estimation (MMCBE).
Backpropagation-free Network for 3D Test-time Adaptation
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation
Scene graph generation aims to capture detailed spatial and semantic relationships between objects in an image, which is challenging due to incomplete labelling, long-tailed relationship categories, and relational semantic overlap.
RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
The Gal-DINO network is trained and evaluated on approximately 5, 000 visually inspected radio galaxies and their infrared hosts, encompassing both compact and extended radio morphologies.
Deep Learning Approaches for Seizure Video Analysis: A Review
Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting.
A Multimodal Dataset and Benchmark for Radio Galaxy and Infrared Host Detection
We present a novel multimodal dataset developed by expert astronomers to automate the detection and localisation of multi-component extended radio galaxies and their corresponding infrared hosts.
RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the Detection of Extended Radio Galaxies and Infrared Hosts
Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts.
Ranked #1 on
2D Object Detection
on RadioGalaxyNET Dataset
Hyperbolic Audio-visual Zero-shot Learning
Audio-visual zero-shot learning aims to classify samples consisting of a pair of corresponding audio and video sequences from classes that are not present during training.
Ranked #2 on
GZSL Video Classification
on ActivityNet-GZSL (cls)
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels
The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts.
Topological Deep Learning: A Review of an Emerging Paradigm
The summaries obtained by these methods are principled global descriptions of multi-dimensional data whilst exhibiting stable properties such as robustness to deformation and noise.
Boundary-aware Instance Segmentation
In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal.
Learning to Co-Generate Object Proposals With a Deep Structured Network
In particular, we introduce a deep structured network that jointly predicts the objectness scores and the bounding box locations of multiple object candidates.
Structural Kernel Learning for Large Scale Multiclass Object Co-Detection
To exploit the correlations between objects, we build a fully-connected CRF on the candidates, which explicitly incorporates both geometric layout relations across object classes and similarity relations across multiple images.
