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Found 46 papers, 14 papers with code
DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation
In fully automatic mode, the presence of inevitable poor prompts (such as points outside the mask or boxes significantly larger than the mask) can significantly mislead mask generation.
Rethinking k-means from manifold learning perspective
Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points.
Multi-View Clustering via Semi-non-negative Tensor Factorization
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability.
Towards Regression-Free Neural Networks for Diverse Compute Platforms
With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important.
Data-driven Attention and Data-independent DCT based Global Context Modeling for Text-independent Speaker Recognition
In this study, a general global time-frequency context modeling framework is proposed to leverage the context information specifically for speaker representation modeling.
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training
We present a method to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models
We propose a memory efficient method, named Stochastic Backpropagation (SBP), for training deep neural networks on videos.
MeMOT: Multi-Object Tracking with Memory
We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span.
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions
Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face.
An Automatic Detection Method Of Cerebral Aneurysms In Time-Of-Flight Magnetic Resonance Angiography Images Based On Attention 3D U-Net
Conclusions:Compared with the results of our previous studies and other studies, the method in this paper achieves a very competitive sensitivity with less training data and maintains a low false positive rate. As the only method currently using 3D U-Net for aneurysm detection, it proves the feasibility and superior performance of this network in aneurysm detection, and also explores the potential of the channel attention mechanism in this task.
Self-supervised Contrastive Attributed Graph Clustering
Thus, for the OOS nodes, SCAGC can directly calculate their clustering labels.
Path Auxiliary Proposal for MCMC in Discrete Space
Energy-based Model (EBM) offers a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions.
Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection
In this paper, we present a novel speaker diarization system for streaming on-device applications.
Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps & CHiME-4 Corpora
With additional techniques such as pronunciation and silence probability modeling, plus multi-style training, we achieve a +5. 42% and +3. 18% relative WER improvement for the development and evaluation sets of the Fearless Steps Corpus.
Effective and Efficient Graph Learning for Multi-view Clustering
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks.
Long Short-Term Transformer for Online Action Detection
We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data.
Ranked #2 on
Online Action Detection
on TVSeries
Semi-TCL: Semi-Supervised Track Contrastive Representation Learning
We design a new instance-to-track matching objective to learn appearance embedding that compares a candidate detection to the embedding of the tracks persisted in the tracker.
Learning Hierarchical Graph Neural Networks for Image Clustering
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
Harnessing Unrecognizable Faces for Improving Face Recognition
The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector.
Compatibility-aware Heterogeneous Visual Search
Existing systems use the same embedding model to compute representations (embeddings) for the query and gallery images.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
Optical manipulation of electronic dimensionality in a quantum material
Exotic phenomenon can be achieved in quantum materials by confining electronic states into two dimensions.
Strongly Correlated Electrons Materials Science Superconductivity
Learning Self-Consistency for Deepfake Detection
We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning
First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples.
DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning
Despite speaker verification has achieved significant performance improvement with the development of deep neural networks, domain mismatch is still a challenging problem in this field.
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
SMOT: Single-Shot Multi Object Tracking
We combine this scheme with SSD detectors by proposing a novel tracking anchor assignment module.
3D-Aided Data Augmentation for Robust Face Understanding
Data augmentation has been highly effective in narrowing the data gap and reducing the cost for human annotation, especially for tasks where ground truth labels are difficult and expensive to acquire.
Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias
In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available.
Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification
Forensic audio analysis for speaker verification offers unique challenges due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings.
Speaker Representation Learning using Global Context Guided Channel and Time-Frequency Transformations
In this study, we propose the global context guided channel and time-frequency transformations to model the long-range, non-local time-frequency dependencies and channel variances in speaker representations.
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power.
SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping
The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications.
Towards causal benchmarking of bias in face analysis algorithms
To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e. g., gender and skin tone, in order to reveal causal links between attribute variation and performance change.
On Improving Temporal Consistency for Online Face Liveness Detection
In this paper, we focus on improving the online face liveness detection system to enhance the security of the downstream face recognition system.
Towards Backward-Compatible Representation Learning
Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods.
Cross-lingual Text-independent Speaker Verification using Unsupervised Adversarial Discriminative Domain Adaptation
Speaker verification systems often degrade significantly when there is a language mismatch between training and testing data.
Sound Event Detection in Multichannel Audio using Convolutional Time-Frequency-Channel Squeeze and Excitation
In this study, we introduce a convolutional time-frequency-channel "Squeeze and Excitation" (tfc-SE) module to explicitly model inter-dependencies between the time-frequency domain and multiple channels.
Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation
These advantages of thermal infrared cameras make the segmentation of semantic objects in day and night.
Analyses of Multi-collection Corpora via Compound Topic Modeling
The usability study on some real-world corpora illustrates the superiority of cLDA to explore the underlying topics automatically but also model their connections and variations across multiple collections.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Learning Robust Search Strategies Using a Bandit-Based Approach
However, choosing or designing a good search heuristic is non-trivial and is often a manual process.
Correlation Heuristics for Constraint Programming
We evaluate our correlation heuristics with well known heuristics, namely, dom/wdeg, impact-based search and activity-based search.
CNN: Single-label to Multi-label
Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks.
Subcategory-Aware Object Classification
In this paper, we introduce a subcategory-aware object classification framework to boost category level object classification performance.
