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Found 27 papers, 16 papers with code
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task.
JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images
These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.
MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation
We propose Masked-Attention Transformers for Surgical Instrument Segmentation (MATIS), a two-stage, fully transformer-based method that leverages modern pixel-wise attention mechanisms for instrument segmentation.
Towards Holistic Surgical Scene Understanding
Second, we present Transformers for Action, Phase, Instrument, and steps Recognition (TAPIR) as a strong baseline for surgical scene understanding.
Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022
We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification.
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input.
Generalized Real-World Super-Resolution through Adversarial Robustness
Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks.
Towards Robust General Medical Image Segmentation
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data.
MIcro-Surgical Anastomose Workflow recognition challenge report
The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels.
SIMBA: Specific Identity Markers for Bone Age Assessment
With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment.
MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition
It achieves an F1-score of 96. 25% in the binary classification and 52. 79% in the eight-class classification.
Rethinking Clustering for Robustness
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness.
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks
Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
Gabor Layers Enhance Network Robustness
We revisit the benefits of merging classical vision concepts with deep learning models.
SMIT: Stochastic Multi-Label Image-to-Image Translation
This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality (e. g., style) associated with the translation.
Dynamic Multimodal Instance Segmentation guided by natural language queries
We address the problem of segmenting an object given a natural language expression that describes it.
The 2017 DAVIS Challenge on Video Object Segmentation
The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques.
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
Deep Retinal Image Understanding
This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation.
Convolutional Oriented Boundaries
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs).
Oracle MCG: A first peek into COCO Detection Challenges
The recently presented COCO detection challenge will most probably be the reference benchmark in object detection in the next years.
Inferring 3D Object Pose in RGB-D Images
The goal of this work is to replace objects in an RGB-D scene with corresponding 3D models from a library.
Hypercolumns for Object Segmentation and Fine-grained Localization
Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation.
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features.
Ranked #6 on
Object Detection In Indoor Scenes
on SUN RGB-D
Simultaneous Detection and Segmentation
Unlike classical semantic segmentation, we require individual object instances.
Ranked #4 on
Object Detection
on PASCAL VOC 2012
