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Found 62 papers, 14 papers with code
A-ACT: Action Anticipation through Cycle Transformations
To incorporate this ability in intelligent systems a question worth pondering upon is how exactly do we anticipate?
Zero-Query Transfer Attacks on Context-Aware Object Detectors
We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled.
Controllable Dynamic Multi-Task Architectures
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
Context-Aware Transfer Attacks for Object Detection
In this paper, we present a new approach to generate context-aware attacks for object detectors.
ADC: Adversarial attacks against object Detection that evade Context consistency checks
A very recent defense strategy for detecting adversarial examples, that has been shown to be robust to current attacks, is to check for intrinsic context consistencies in the input data, where context refers to various relationships (e. g., object-to-object co-occurrence relationships) in images.
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied.
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID).
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples.
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning
Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution.
Learning Few-shot Open-set Classifiers using Exemplar Reconstruction
We study the problem of how to identify samples from unseen categories (open-set classification) when there are only a few samples given from the seen categories (few-shot setting).
Deep Quantized Representation for Enhanced Reconstruction
In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana.
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
Cross-domain Imitation from Observations
Once this correspondence is found, we can directly transfer the demonstrations on one domain to the other and use it for imitation.
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data.
Detection and Localization of Facial Expression Manipulations
Thus, it is important to develop methods that can detect manipulations in facial expressions, and localize the manipulated regions.
Learning to identify image manipulations in scientific publications
We show that our method leads to a 90% accuracy rate of detecting duplicated images, a ~ 13% improvement in detection accuracy in comparison to other manipulation detection methods.
Exploiting Context for Robustness to Label Noise in Active Learning
Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available.
ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation
Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry.
Measurement-driven Security Analysis of Imperceptible Impersonation Attacks
The emergence of Internet of Things (IoT) brings about new security challenges at the intersection of cyber and physical spaces.
Text-based Localization of Moments in a Video Corpus
This task poses a unique challenge as the system is required to perform: (i) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, and (ii) temporal localization of moment in the relevant video based on sentence query.
Adversarial Knowledge Transfer from Unlabeled Data
While machine learning approaches to visual recognition offer great promise, most of the existing methods rely heavily on the availability of large quantities of labeled training data.
Distributed Multi-agent Video Fast-forwarding
In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness.
Domain Adaptive Semantic Segmentation Using Weak Labels
In this work, we propose a novel framework for domain adaptation in semantic segmentation with image-level weak labels in the target domain.
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Most of the existing approaches for person re-identification consider a static setting where the number of cameras in the network is fixed.
Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification
While supervised techniques in re-identification are extremely effective, the need for large amounts of annotations makes them impractical for large camera networks.
Learning Person Re-identification Models from Videos with Weak Supervision
In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision.
Multiple Instance Learning
Video-Based Person Re-Identification
Non-Adversarial Video Synthesis with Learned Priors
Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.
Learning from Trajectories via Subgoal Discovery
Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples.
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification
Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network.
Prediction and Description of Near-Future Activities in Video
Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations.
A Skip Connection Architecture for Localization of Image Manipulations
In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection.
Context-Aware Query Selection for Active Learning in Event Recognition
We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human.
Weakly Supervised Video Moment Retrieval From Text Queries
The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate.
Detecting GAN generated Fake Images using Co-occurrence Matrices
The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images.
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones.
Webly Supervised Joint Embedding for Cross-Modal lmage-Text Retrieval
Inspired by the recent success of web-supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation.
Multi-View Frame Reconstruction with Conditional GAN
Multi-view frame reconstruction is an important problem particularly when multiple frames are missing and past and future frames within the camera are far apart from the missing ones.
Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval
Inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation.
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts.
Incorporating Scalability in Unsupervised Spatio-Temporal Feature Learning
This necessitates learning of visual features from videos in an unsupervised setting.
W-TALC: Weakly-supervised Temporal Activity Localization and Classification
Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement.
Ranked #1 on
Action Classification
on ActivityNet-1.2
Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval
Constructing a joint representation invariant across different modalities (e. g., video, language) is of significant importance in many multimedia applications.
Ranked #15 on
Video Retrieval
on MSR-VTT
Exploiting Transitivity for Learning Person Re-Identification Models on a Budget
Minimization of labeling effort for person re-identification in camera networks is an important problem as most of the existing popular methods are supervised and they require large amount of manual annotations, acquiring which is a tedious job.
FFNet: Video Fast-Forwarding via Reinforcement Learning
The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users.
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods.
Weakly Supervised Summarization of Web Videos
Casting the problem as a weakly supervised learning problem, we propose a flexible deep 3D CNN architecture to learn the notion of importance using only video-level annotation, and without any human-crafted training data.
Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos
We propose a network similar to a hybrid Siamese network with three branches to jointly learn both the future label and the starting time.
Exploiting Spatial Structure for Localizing Manipulated Image Regions
In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions.
Detection and Localization of Image Forgeries using Resampling Features and Deep Learning
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning.
The Impact of Typicality for Informative Representative Selection
In computer vision, selection of the most informative samples from a huge pool of training data in order to learn a good recognition model is an active research problem.
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
In this paper, with the aim of summarizing multi-view videos, we introduce a novel unsupervised framework via joint embedding and sparse representative selection.
Collaborative Summarization of Topic-Related Videos
Large collections of videos are grouped into clusters by a topic keyword, such as Eiffel Tower or Surfing, with many important visual concepts repeating across them.
Diversity-aware Multi-Video Summarization
Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos.
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information.
Video Summarization in a Multi-View Camera Network
While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space.
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer vision.
Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection
We demonstrate the effectiveness of our approach on multi-camera person re-identification datasets, to demonstrate the feasibility of learning online classification models in multi-camera big data applications.
Learning Temporal Regularity in Video Sequences
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
Ranked #2 on
Traffic Accident Detection
on A3D
Context Aware Active Learning of Activity Recognition Models
We formulate a conditional random field (CRF) model that encodes the context and devise an information theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative query instances, which need to be labeled by a human.
Incremental Activity Modeling and Recognition in Streaming Videos
Most of the state-of-the-art approaches to human activity recognition in video need an intensive training stage and assume that all of the training examples are labeled and available beforehand.
Context-Aware Modeling and Recognition of Activities in Video
This is motivated from the observations that the activities related in space and time rarely occur independently and can serve as the context for each other.
Information Consensus for Distributed Multi-target Tracking
The estimation errors in tracking and data association, as well as the effect of naivety, are jointly addressed leading to the development of an informationweighted consensus algorithm, which we term as the Multitarget Information Consensus (MTIC) algorithm.
