Video Representation Research Articles

Self-Supervised Video Representation Learning by Serial Restoration With Elastic Complexity

Self-Supervised Video Representation Learning by Serial Restoration With Elastic Complexity

Self-Supervised Video Representation Learning by Video Incoherence Detection.

This article introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It stems from the observation that the visual system of human beings can easily identify video incoherence based on their comprehensive understanding of videos. Specifically, we construct the incoherent clip by multiple subclips hierarchically sampled from the same raw video with various lengths of incoherence. The network is trained to learn the high-level representation by predicting the location and length of incoherence given the incoherent clip as input. Additionally, we introduce intravideo contrastive learning to maximize the mutual information between incoherent clips from the same raw video. We evaluate our proposed method through extensive experiments on action recognition and video retrieval using various backbone networks. Experiments show that our proposed method achieves remarkable performance across different backbone networks and different datasets compared to previous coherence-based methods.

(Un)likelihood Training for Interpretable Embedding

Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult, if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video understanding due to unexplainable and unpredictable results. In this article, we propose two novel training objectives, likelihood and unlikelihood functions, to unroll the semantics behind embeddings while addressing the label sparsity problem in training. The likelihood training aims to interpret semantics of embeddings beyond training labels, while the unlikelihood training leverages prior knowledge for regularization to ensure semantically coherent interpretation. With both training objectives, a new encoder-decoder network, which learns interpretable cross-modal representation, is proposed for ad-hoc video search. Extensive experiments on TRECVid and MSR-VTT datasets show that the proposed network outperforms several state-of-the-art retrieval models with a statistically significant performance margin.

Deep video representation learning: a survey

Deep video representation learning: a survey

SOFT: Self-supervised sparse Optical Flow Transformer for video stabilization via quaternion

Video stabilization is crucial for video representation learning, which suffers from the challenges such as the perception of unstable vision, the stripping and cognition of target motion features in complex scenes, the correction of the jittery camera systems trails. In this paper, we propose a Self-supervised sparse Optical Flow Transformer (SOFT) model, consisting of a self-supervised contrastive learning transformer network, a sparse optical flow perception network and a multimodal cognitive fusion network. The SOFT model takes advantage of optical flow to estimate motion. The sparse optical flow perception network perceiving partially sparse optical flow containing motion features. This serves as the input to the self-supervised contrastive learning transformer network for generating sparse optical flow features, which are fed into the multimodal cognitive fusion network together with the real and virtual camera pose for video frame warping. Experimental comparisons with state-of-the-art models on 4 metrics demonstrate the effectiveness of the SOFT model. It achieves the best performance with an average Stability of 0.869 and average Distortion of 0.993 across 6 categories videos, which shows that the SOFT model can effectively perceive the motion in the video and smooth the jitter track of videos.

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues.

A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.

Knowledge-guided pre-training and fine-tuning: Video representation learning for action recognition

Video-based action recognition is an important task in the computer vision community, aiming to extract rich spatial–temporal information to recognize human actions from videos. Many approaches adopt self-supervised learning in large-scale unlabeled datasets and exploit transfer learning in the downstream action recognition task. Though much progress has been made for action recognition with video representation learning, two main issues remain for most existing methods. Firstly, the pre-training with self-supervised pretext tasks usually learns neutral and not much informative representations for the downstream action recognition task. Secondly, the valuable learned knowledge from large-scaled pre-training datasets will be gradually forgotten in the fine-tuning stage. To address such issues, in this paper, we propose a novel video representation learning method with knowledge-guided pre-training and fine-tuning for action recognition, which incorporates external human parsing knowledge for generating informative representation in the pre-training, and preserves the pre-trained knowledge in the fine-tuning stage to avoid catastrophic forgetting via self-distillation. Our model, with contributions from the external human parsing knowledge, video-level contrastive learning, and knowledge preserving self-distillation, achieves state-of-the-art performance on two popular benchmarks, i.e., UCF101 and HMDB51, verifying the effectiveness of the proposed method.

BiC-Net: Learning Efficient Spatio-temporal Relation for Text-Video Retrieval

The task of text-video retrieval aims to understand the correspondence between language and vision and has gained increasing attention in recent years. Recent works have demonstrated the superiority of local spatio-temporal relation learning with graph-based models. However, most existing graph-based models are handcrafted and depend heavily on expert knowledge and empirical feedback, which may be unable to mine the high-level fine-grained visual relations effectively. These limitations result in their inability to distinguish videos with the same visual components but different relations. To solve this problem, we propose a novel cross-modal retrieval framework, Bi-Branch Complementary Network (BiC-Net), which modifies Transformer architecture to effectively bridge text-video modalities in a complementary manner via combining local spatio-temporal relation and global temporal information. Specifically, local video representations are encoded using multiple Transformer blocks and additional residual blocks to learn fine-grained spatio-temporal relations and long-term temporal dependency, calling the module a Fine-grained Spatio-temporal Transformer (FST). Global video representations are encoded using a multi-layer Transformer block to learn global temporal features. Finally, we align the spatio-temporal relation and global temporal features with the text feature on two embedding spaces for cross-modal text-video retrieval. Extensive experiments are conducted on MSR-VTT, MSVD, and YouCook2 datasets. The results demonstrate the effectiveness of our proposed model. Our code is public at https://github.com/lionel-hing/BiC-Net .

Weakly-Supervised Video Anomaly Detection with MTDA-Net

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.

Actor-Aware Self-Supervised Learning for Semi-Supervised Video Representation Learning

Self-supervised contrastive learning has shown a significant improvement in performance for action recognition tasks by discovering useful signals from unlabeled videos. Nevertheless, the unique features of existing video benchmark datasets have led the learned video representations to be contextually biased toward dominant backgrounds and scene correlations. Thus, ultimately leading to poor generalizations on scene-invariant action recognition. Therefore, we propose Actor-aware Self-supervised Learning for Semi-supervised Video Representation Learning (ActorSL). We aligned localized actors and their corresponding scene information to encourage the model to learn discriminative regions and mitigate the model’s dependency on the video background during contrastive training. Furthermore, we present an inter-video Background Mixing (iBM) augmentation strategy to introduce scene consistency into the model. We patch inter-video crops of four randomly selected frames for iBM to create a unique frame for each video. The patched frame is blended with the target video frames to generate a spatially augmented sample. Then, the actor-scene aligned features and features of iBM-augmented videos are utilized to optimize contrastive loss and consistency regularization jointly in a semi-supervised way. Moreover, iBM combines the one-hot-encoded labels of patches with the label of the target video as a label smoothing regularizer to soften the decision boundaries of the semi-supervised model. Our experimental results reveal that, ActorSL notably improved current state-of-the-art semi-supervised methods on the Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime. Code released at https://github.com/Endarzboy/ActorSL.

Learning Spatio-Temporal Semantics and Cluster Relation for Zero-Shot Action Recognition

Zero-shot Action Recognition (ZSAR) aims at bridging the video→class relation with only labeled training data of seen classes while generalizing the model to alleviate the heterogeneity of unseen actions. Most existing methods have comprehensively represented videos and action classes, however, the semantic gap and the hubness problem between them remain crucial challenges that are under-explored. In this paper, we propose an effective method to tackle the above issues. Specifically, to narrow the semantic gap, we end-to-end generate a spatio-temporal semantics for each video, which provides essential textual information to refine the video representation. Furthermore, we propose a compactness-separability loss that optimizes the intra- and inter-class relations in a unified formula and quantitatively constrains cluster distribution, thus effectively diminishing the impact of the hubness problem. Extensive experiments on UCF101, HMDB51, and Olympic Sports datasets prove the effectiveness of the proposed approach and demonstrate our approach outperforms the state-of-the-art methods.

CMGNet: Collaborative multi-modal graph network for video captioning

In video captioning, it is very challenging to comprehensively describe multi-modal content information of a video, such as appearance, motion, and object. Prior arts often neglect interactions among multiple modalities and thus their video representations may not fully depict scene contents. In this paper, we propose a collaborative multi-modal graph network (CMGNet) to explore the interactions among multi-modal features in video captioning. Our CMGNet is composed of an encoder–decoder structure: a Compression-driven Intra-inter Attentive Graph (CIAG) encoder and an Adaptive Multi-modal Selection (AMS) decoder. Specifically, in our CIAG encoder, we first design a Basis Vector Compression (BVC) module to reduce the redundant nodes in graphs and thus improve the efficiency in coping with a large number of nodes. Then we propose an Intra-inter Attentive Graph (IAG) to improve the graph representation by sharing information across intra-and-inter nodes. Afterwards, we present an AMS decoder to generate video captions from the encoded video res presentations. In particular, we let the proposed AMS decoder learn to produce words by adaptively focusing on different modality information, thus leading to comprehensive and accurate captions. Extensive experiments on the large-scale benchmarks, i.e., MSR-VTT and TGIF, demonstrate that our proposed CMGNet achieves the state-of-the-art.

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different resolutions and encoded at different bitrates. One key issue in omnidirectional video streaming is how to choose the optimal representations for each tile at the server to save the overall transmission bitrate to all users while offering them satisfactory quality. This is different from the adaptive bitrate-based method that optimizes the downloading procedure of individual users, where the given video representations are stored on the server. In this work, we focus on optimization for the encoding of omnidirectional video streaming by using the optimal combination of tile representations. To achieve our goal, we formulate the selection of the representations into an optimization problem in which the transmission bitrate of all the representations is minimized with a quality constraint. By using this constraint, we can improve the average quality of omnidirectional videos for users. More specifically, we first construct the tile-level rate-distortion (R-D) model and determine the available tile bandwidth based on the previous viewers’ statistics. Then, we formulate the representation selection problem based on the obtained R-D model and tile bandwidth. Finally, we solve this problem to obtain the optimal combination of tile representations so that we can transmit the omnidirectional video to users with satisfactory quality but low bitrate. The experimental results demonstrate the effectiveness of our proposed approach when it is applied to omnidirectional video streaming.

Task-Aware Dual-Representation Network for Few-Shot Action Recognition

Few-shot action recognition has attracted increasing attention in recent years, but it remains challenging due to the intrinsic difficulty in learning transferable knowledge to generalize to novel classes by using a few labeled samples. Although some successful progress has been made, most few-shot action recognition methods commonly focus on the global characteristics of samples while ignoring the local characteristics of samples, which results in the weak generalization ability of the model. In this paper, we propose a task-aware dual-representation network (TADRNet) for few-shot action recognition, which learns how to adapt video representations to novel tasks in a meta-learning manner. It mainly includes a global relational graph subnetwork (GRG) and a fine-grained local representation subnetwork (FLR). Our method simultaneously considers both global and local characteristics of samples for few-shot action recognition. From a global perspective, we propose GRG to explore the relations across support-query sample pairs by using the relational graph neural network. To facilitate the few-shot visual learning, we propose a novel hybrid semantic attention module (HSA) for enhancing the discriminability of support and query features. From a local perspective, we utilize FLR to fully exploit the local characteristics of samples, which can improve the classification results obtained by GRG and thus guarantee high classification accuracy. Extensive experiments on four challenging benchmarks show that the proposed TADRNet significantly outperforms a variety of state-of-the-art few-shot action recognition methods.

Similarity contrastive estimation for image and video soft contrastive self-supervised learning

Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that are considered as noise. However, several instances in a dataset are drawn from the same distribution and share underlying semantic information. A good data representation should contain relations between the instances, or semantic similarity and dissimilarity, that contrastive learning harms by considering all negatives as noise. To circumvent this issue, we propose a novel formulation of contrastive learning using semantic similarity between instances called Similarity Contrastive Estimation (SCE). Our training objective is a soft contrastive one that brings the positives closer and estimates a continuous distribution to push or pull negative instances based on their learned similarities. We validate empirically our approach on both image and video representation learning. We show that SCE performs competitively with the state of the art on the ImageNet linear evaluation protocol for fewer pretraining epochs and that it generalizes to several downstream image tasks. We also show that SCE reaches state-of-the-art results for pretraining video representation and that the learned representation can generalize to video downstream tasks. Source code is available here: https://github.com/juliendenize/eztorch.

On learning distribution alignment for video-based visible-infrared person re-identification

This paper studies the matching problem of cross-modality video data from a discrete distribution alignment view. Central to this discussion is the visible-infrared person re-identification (VI-reID), a crucial feature that bolsters surveillance systems’ efficacy in monitoring individuals across diverse lighting conditions. Going beyond traditional image-to-image matching paradigms, a recent study shows that temporal information can bring richer cues to encode the pedestrian representation, improving the representation power of deep neural networks. However, this integration further complicates cross-modality data matching due to the joint processing of spatial and temporal information. This paper formulates the video data as a discrete distribution and aligns the cross-modality video representation by reducing the matching cost between the two distributions. To this end, a natural idea for aligning the videos is to reduce the divergence of distributions. Moreover, the powerful optimal transport (OT) scheme, which generates the optimal matching flows and establishes the relevance of two distributions, is also employed as a way to measure the distance of distributions. Nevertheless, we observe that endowing the OT in the advanced VI-reID feature extractor leads to a non-symmetric measurement. To mitigate this, the paper introduces a new metric, namely symmetric optimal transport (SOT), reformulating OT into a symmetric form. Thorough analyses and empirical studies affirm the superiority of the proposed SOT, which significantly outperforms the current state-of-the-art methods according to standard benchmarking evaluations.

Stochastic video normality network for abnormal event detection in surveillance videos

Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised VAD methods either use a single proxy task to represent the prototypical normal patterns, or split the video into static and dynamic parts and learn spatial and temporal normality separately, which cannot model the inherent uncertainty of motion. In this regard, we propose for the first time to learn video normality via stochastic motion representation. Specifically, the proposed Stochastic Video Normality (SVN) network learns the prototypical local appearance patterns via deterministic multi-task learning and global motion patterns in a non-deterministic manner. The stochastic representation module uses recurrent networks to model historical motion as a conditional Gaussian distribution and infers motion explicitly with the learned prior. Besides, a mask auto-encoder is introduced to explore the relationship between appearance and motion, boosting the model to learn spatial–temporal normality. Experimental results show that our proposed SVN network performs comparably to the state-of-the-art methods. Extensive analysis demonstrates the effectiveness of multi-task learning-based appearance representation and stochastic motion representation for unsupervised video anomaly detection.

Quantification of Parkinsonian unilateral involvement from ocular fixational patterns using a deep video representation

PurposeLateralisation of motor symptoms is a prevalent characteristic of Parkinson’s disease (PD). Hence, unilateral involvement is crucial for personalized treatments and measuring therapy effectiveness. Nonetheless, most motor symptoms, including lateralization, are mainly evident at advanced stages of the disease. Recently, ocular fixation instability emerged as a promising PD biomarker with a high sensitivity to discriminate PD. We hypothesize that unilateral involvement can be recovered from the assessment and quantification of PD-related ocular abnormalities.MethodsThis method proposes a computer-based strategy to quantify PD lateralization from ocular fixation patterns. The method follows a markerless strategy fed by slices with spatiotemporal eye movement information. A deep convolutional model was used to discriminate between PD and a control population. Additionally, model prediction probabilities were analyzed to select the dominant eye associated with unilateral involvement.ResultsThe proposed approach reports an average accuracy of 91.92% classifying PD. Interestingly, using the dominant side, the approach achieves an average PD prediction probability of 93.3% (95% CI: [91.61,95.07]), evidencing capabilities to capture the most affected side. Besides, the reported results strongly correlate with the disease, even for patients categorized at early stages. A low-dimensional projection tool was used to support the classification results by finding a 2d space that eases the discrimination among classes.ConclusionsThe strategy is sensitive to detecting and classifying PD fixational patterns and determining the side with major impairments. This approach may be a potential tool to support the characterization of the disease and as an alternative to defining personalized treatments.

VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building

We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing.

Global semantic enhancement network for video captioning

Video captioning aims to briefly describe the content of a video in accurate and fluent natural language, which is a hot research topic in multimedia processing. As a bridge between video and natural language, video captioning is a challenging task that requires a deep understanding of video content and effective utilization of diverse video multimodal information. Existing video captioning methods usually ignore the relative importance between different frames when aggregating frame-level video features and neglect the global semantic correlations between videos and texts in learning visual representations, resulting in the learned representations less effective. To address these problems, we propose a novel framework, namely Global Semantic Enhancement Network (GSEN) to generate high-quality captions for videos. Specifically, a feature aggregation module based on a lightweight attention mechanism is designed to aggregate frame-level video features, which highlights features of informative frames in video representations. In addition, a global semantic enhancement module is proposed to enhance semantic correlations for video and language representations in order to generate semantically more accurate captions. Extensive qualitative and quantitative experiments on two public benchmark datasets MSVD and MSR-VTT demonstrate that the proposed GSEN can achieve superior performance than state-of-the-art methods.