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Found 25 papers, 4 papers with code
DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention
These descriptors are extracted using DenseNet which significantly improves cross-sensor, cross-material and cross-dataset performance.
Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification
Person re-identification is the task of matching pedestrian images across non-overlapping cameras.
PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets
In order to design haptic icons or build a haptic vocabulary, we require a set of easily distinguishable haptic signals to avoid perceptual ambiguity, which in turn requires a way to accurately estimate the perceptual (dis)similarity of such signals.
Batch Decorrelation for Active Metric Learning
We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$.
Spatio-temporal interaction model for crowd video analysis
We present an unsupervised approach to analyze crowd at various levels of granularity $-$ individual, group and collective.
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition
We show that estimation of this hidden structure corresponds to track association and group detection.
Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification
In this paper we propose a novel metric learning framework called Nullspace Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the small sample size (SSS) problem inherent in person re-identification and offers a significant performance gain over existing state-of-the-art methods.
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection
Typically, the methods based on this approach operate at a fixed timescale - either a single time-instant (eg.
Ranked #2 on
Anomaly Detection
on Corridor
Online Sensor Hallucination via Knowledge Distillation for Multimodal Image Classification
We deal with the problem of information fusion driven satellite image/scene classification and propose a generic hallucination architecture considering that all the available sensor information are present during training while some of the image modalities may be absent while testing.
On the Separability of Classes with the Cross-Entropy Loss Function
The main result of our analysis is the derivation of a lower bound for the probability with which the inter-class distance is more than the intra-class distance in this feature space, as a function of the loss value.
Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification.
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification
In this paper, we propose a semi-supervised metric learning approach that can utilize information in unlabelled data with the help of a few labelled training samples.
Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning
We propose a transductive approach to reduce the effect of domain shift, where we utilize unlabeled visual data from unseen classes to generate corresponding semantic features for unseen class visual samples.
Multi-Source Open-Set Deep Adversarial Domain Adaptation
Besides, an adversarial learning strategy is followed to model the discriminator between the target-domain known and unknown classes.
Enhancing Haptic Distinguishability of Surface Materials with Boosting Technique
Discriminative features are crucial for several learning applications, such as object detection and classification.
A Novel Actor Dual-Critic Model for Remote Sensing Image Captioning
We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning.
GuCNet: A Guided Clustering-based Network for Improved Classification
We deal with the problem of semantic classification of challenging and highly-cluttered dataset.
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation
Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic.
3D-NVS: A 3D Supervision Approach for Next View Selection
We present a classification based approach for the next best view selection and show how we can plausibly obtain a supervisory signal for this task.
A Unified Batch Selection Policy for Active Metric Learning
To avoid redundancy between triplets, our method collectively selects batches with maximum joint entropy, which simultaneously captures both informativeness and diversity.
Deep Learning based Novel View Synthesis
In this work, we propose a deep convolutional neural network (CNN) which learns to predict novel views of a scene from given collection of images.
FRIDA -- Generative Feature Replay for Incremental Domain Adaptation
Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly.
Generative Adversarial Network
Unsupervised Domain Adaptation
Prototypical quadruplet for few-shot class incremental learning
We demonstrate the effectiveness of our method by showing that the embedding space remains intact after training the model with new classes and outperforms existing state-of-the-art algorithms in terms of accuracy across different sessions.
MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation
Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications.
Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach
This paper proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one.
