Search Results for author:
Found 132 papers, 86 papers with code
Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages
An old-school recipe for training a classifier is to (i) learn a good feature extractor and (ii) optimize a linear layer atop.
Group-robust Machine Unlearning
MIU minimizes the mutual information between model features and group information, achieving unlearning while reducing performance degradation in the dominant group of the forget set.
One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering
To mitigate this issue, we introduce a pseudo-rehearsal balancing module that aligns the generated data towards the ground-truth data distribution using either the question meta-statistics or an unsupervised clustering method.
Retrieval-enriched zero-shot image classification in low-resource domains
Low-resource domains, characterized by scarce data and annotations, present significant challenges for language and visual understanding tasks, with the latter much under-explored in the literature.
Organizing Unstructured Image Collections using Natural Language
Our framework, Text Driven Semantic Multiple Clustering (TeDeSC), uses text as a proxy to concurrently reason over large image collections, discover partitioning criteria, expressed in natural language, and reveal semantic substructures.
AL-GTD: Deep Active Learning for Gaze Target Detection
In this paper, our goal is to reduce the reliance on the size of labeled training data for gaze target detection.
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models
Secondly, we benchmark the zero-shot object classification performance of state-of-the-art vision-language models (VLMs) on the proposed MIMEX dataset.
Text-Enhanced Zero-Shot Action Recognition: A training-free approach
Vision-language models (VLMs) have demonstrated remarkable performance across various visual tasks, leveraging joint learning of visual and textual representations.
The Phantom Menace: Unmasking Privacy Leakages in Vision-Language Models
Vision-Language Models (VLMs) combine visual and textual understanding, rendering them well-suited for diverse tasks like generating image captions and answering visual questions across various domains.
One-Shot Unlearning of Personal Identities
To this extent, existing MU approaches assume complete or partial access to the training data, which can be limited over time due to privacy regulations.
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization
Previous works primarily focus on prompt learning to adapt the CLIP into a variety of downstream tasks, however, suffering from task overfitting when fine-tuned on a small data set.
Automatic benchmarking of large multimodal models via iterative experiment programming
Finally, the LLM refines the report, presenting the results to the user in natural language.
Frustratingly Easy Test-Time Adaptation of Vision-Language Models
Thanks to its simplicity and comparatively negligible computation, ZERO can serve as a strong baseline for future work in this field.
Less is more: Summarizing Patch Tokens for efficient Multi-Label Class-Incremental Learning
Since independent pathways in truly incremental scenarios will result in an explosion of computation due to the quadratically complex multi-head self-attention (MSA) operation in prompt tuning, we propose to reduce the original patch token embeddings into summarized tokens.
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference.
Vocabulary-free Image Classification and Semantic Segmentation
To address VIC, we propose Category Search from External Databases (CaSED), a training-free method that leverages a pre-trained vision-language model and an external database.
Socially Pertinent Robots in Gerontological Healthcare
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary.
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
Test-Time Zero-Shot Temporal Action Localization
To this aim, we introduce a novel method that performs Test-Time adaptation for Temporal Action Localization (T3AL).
Harnessing Large Language Models for Training-free Video Anomaly Detection
Video anomaly detection (VAD) aims to temporally locate abnormal events in a video.
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis
Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability.
Ranked #1 on
Text-to-Video Generation
on MSR-VTT
Democratizing Fine-grained Visual Recognition with Large Language Models
Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR).
Novel class discovery meets foundation models for 3D semantic segmentation
Firstly, it introduces the novel task of NCD for point cloud semantic segmentation.
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.
Incremental Object-Based Novelty Detection with Feedback Loop
Object-based Novelty Detection (ND) aims to identify unknown objects that do not belong to classes seen during training by an object detection model.
Delving into CLIP latent space for Video Anomaly Recognition
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
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.
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers
State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forgetting.
The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation
Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
Object-aware Gaze Target Detection
Gaze target detection aims to predict the image location where the person is looking and the probability that a gaze is out of the scene.
Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations
This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to labels.
Semi-supervised learning made simple with self-supervised clustering
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations.
Vocabulary-free Image Classification
We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.
Rotation Synchronization via Deep Matrix Factorization
In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively.
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
To answer this question, we design the first experimental setup for studying domain generalization (DG) for LiDAR semantic segmentation (DG-LSS).
Exploring Diffusion Models for Unsupervised Video Anomaly Detection
This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used.
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source.
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery
Discovering novel concepts in unlabelled datasets and in a continuous manner is an important desideratum of lifelong learners.
Quantum Multi-Model Fitting
Geometric model fitting is a challenging but fundamental computer vision problem.
Novel Class Discovery for 3D Point Cloud Semantic Segmentation
Firstly, we address the new problem of NCD for point cloud semantic segmentation.
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference.
Simplifying Open-Set Video Domain Adaptation with Contrastive Learning
In this work we address a more realistic scenario, called open-set video domain adaptation (OUVDA), where the target dataset contains "unknown" semantic categories that are not shared with the source.
A soft nearest-neighbor framework for continual semi-supervised learning
Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data.
ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available.
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding.
Cross-Domain Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions.
Multimodal Across Domains Gaze Target Detection
This paper addresses the gaze target detection problem in single images captured from the third-person perspective.
Uncertainty-guided Source-free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.
Unsupervised Domain Adaptation for Video Transformers in Action Recognition
On the other hand, the performance of a model in action recognition is heavily affected by domain shift.
Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss
Our method, as being based on contrastive loss between pairwise modalities, is the first attempt in MER literature.
Cultural Vocal Bursts Intensity Prediction
Multimodal Emotion Recognition
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.
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.
Class-incremental Novel Class Discovery
We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories.
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.
Quantum Motion Segmentation
Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.
Playable Environments: Video Manipulation in Space and Time
We present Playable Environments - a new representation for interactive video generation and manipulation in space and time.
3D Object Detection from Images for Autonomous Driving: A Survey
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years.
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift.
Graph-based Generative Face Anonymisation with Pose Preservation
With the goal to maintain the geometric attributes of the source face, i. e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model.
Self-Supervised Models are Continual Learners
Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
The global alignment network aims to transfer the input image from the source domain to the target domain.
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
A Unified Objective for Novel Class Discovery
In this paper, we study the problem of Novel Class Discovery (NCD).
Ranked #3 on
Novel Object Detection
on LVIS v1.0 val
Click to Move: Controlling Video Generation with Sparse Motion
This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying simple object trajectories of the key objects in the scene.
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
Neighborhood Contrastive Learning for Novel Class Discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
Transformer-Based Source-Free Domain Adaptation
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.
Ranked #2 on
Multi-target Domain Adaptation
on Office-Home
Blended-target Domain Adaptation
Multi-target Domain Adaptation
Boosting Binary Masks for Multi-Domain Learning through Affine Transformations
In this work, we provide a general formulation of binary mask based models for multi-domain learning by affine transformations of the original network parameters.
Inferring Latent Domains for Unsupervised Deep Domain Adaptation
Most deep UDA approaches operate in a single-source, single-target scenario, i. e. they assume that the source and the target samples arise from a single distribution.
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #8 on
Depth Estimation
on NYU-Depth V2
Variational Structured Attention Networks for Deep Visual Representation Learning
Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).
Playable Video Generation
This paper introduces the unsupervised learning problem of playable video generation (PVG).
Multi-Domain Image-to-Image Translation with Adaptive Inference Graph
Therefore, we propose to increase the network capacity by using an adaptive graph structure.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
Viewing Graph Solvability via Cycle Consistency
In structure-from-motion the viewing graph is a graph where vertices correspond to cameras and edges represent fundamental matrices.
Variational Structured Attention Networks for Dense Pixel-Wise Prediction
State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.
Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?
Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split.
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert.
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).
Deep Variational Generative Models for Audio-visual Speech Separation
To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data.
Learning to Cluster under Domain Shift
While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i. e. labeled source data must be available.
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).
Online Continual Learning under Extreme Memory Constraints
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences.
Towards Recognizing Unseen Categories in Unseen Domains
The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training.
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set.
TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
Motion-supervised Co-Part Segmentation
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
Ranked #3 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
First Order Motion Model for Image Animation
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
Modeling the Background for Incremental Learning in Semantic Segmentation
Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i. e. pixels that do not belong to any other classes) exhibit a semantic distribution shift.
Ranked #3 on
Domain 11-5
on Cityscapes
Towards Generalization Across Depth for Monocular 3D Object Detection
While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind.
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize.
Budget-Aware Adapters for Multi-Domain Learning
To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Our proposal is evaluated on the wellestablished KITTI dataset, where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others.
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs
The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines.
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth.
Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
Animating Arbitrary Objects via Deep Motion Transfer
This is achieved through a deep architecture that decouples appearance and motion information.
Enhancing Perceptual Attributes with Bayesian Style Generation
Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute.
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other.
Kitting in the Wild through Online Domain Adaptation
This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified.
Best sources forward: domain generalization through source-specific nets
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions.
Ranked #122 on
Domain Generalization
on PACS
Robust Place Categorization with Deep Domain Generalization
Our method develops from the intuition that, given a set of different classification models associated to known domains (e. g. corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models.
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Visual recognition algorithms are required today to exhibit adaptive abilities.
Boosting Domain Adaptation by Discovering Latent Domains
Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution.
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation
Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks.
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval
In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR).
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Depth cues have been proved very useful in various computer vision and robotic tasks.
Every Smile is Unique: Landmark-Guided Diverse Smile Generation
Finally, these landmark sequences are translated into face videos.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.
AutoDIAL: Automatic DomaIn Alignment Layers
Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one.
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This paper addresses the problem of depth estimation from a single still image.
Ranked #14 on
Depth Estimation
on NYU-Depth V2
How to Make an Image More Memorable? A Deep Style Transfer Approach
In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match.
Viraliency: Pooling Local Virality
In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community.
Learning Deep NBNN Representations for Robust Place Categorization
This paper presents an approach for semantic place categorization using data obtained from RGB cameras.
Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation
The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion
A very popular approach for transductive multi-label recognition under linear classification settings is matrix completion.
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions
Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR).
Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos
We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes.
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.
