DiffusionVMR: Diffusion Model for Video Moment Retrieval

Video moment retrieval is a fundamental visual-language task that aims to retrieve target moments from an untrimmed video based on a language query. Existing methods typically generate numerous proposals manually or via generative networks in advance as the support set for retrieval, which is not only inflexible but also time-consuming. Inspired by the success of diffusion models on object detection, this work aims at reformulating video moment retrieval as a denoising generation process to get rid of the inflexible and time-consuming proposal generation. To this end, we propose a novel proposal-free framework, namely DiffusionVMR, which directly samples random spans from noise as candidates and introduces denoising learning to ground target moments. During training, Gaussian noise is added to the real moments, and the model is trained to learn how to reverse this process. In inference, a set of time spans is progressively refined from the initial noise to the final output. Notably, the training and inference of DiffusionVMR are decoupled, and an arbitrary number of random spans can be used in inference without being consistent with the training phase. Extensive experiments conducted on three widely-used benchmarks (i.e., QVHighlight, Charades-STA, and TACoS) demonstrate the effectiveness of the proposed DiffusionVMR by comparing it with state-of-the-art methods

Open-Vocabulary Object Detection via Scene Graph Discovery

In recent years, open-vocabulary (OV) object detection has attracted increasing research attention. Unlike traditional detection, which only recognizes fixed-category objects, OV detection aims to detect objects in an open category set. Previous works often leverage vision-language (VL) training data (e.g., referring grounding data) to recognize OV objects. However, they only use pairs of nouns and individual objects in VL data, while these data usually contain much more information, such as scene graphs, which are also crucial for OV detection. In this paper, we propose a novel Scene-Graph-Based Discovery Network (SGDN) that exploits scene graph cues for OV detection. Firstly, a scene-graph-based decoder (SGDecoder) including sparse scene-graph-guided attention (SSGA) is presented. It captures scene graphs and leverages them to discover OV objects. Secondly, we propose scene-graph-based prediction (SGPred), where we build a scene-graph-based offset regression (SGOR) mechanism to enable mutual enhancement between scene graph extraction and object localization. Thirdly, we design a cross-modal learning mechanism in SGPred. It takes scene graphs as bridges to improve the consistency between cross-modal embeddings for OV object classification. Experiments on COCO and LVIS demonstrate the effectiveness of our approach. Moreover, we show the ability of our model for OV scene graph detection, while previous OV scene graph generation methods cannot tackle this task

Less than Few: Self-Shot Video Instance Segmentation

The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labelling a few examples appears unrealistic. This is especially true as the level of details in spatio-temporal video understanding and with it, the complexity of annotations continues to increase. Rather than performing few-shot learning with a human oracle to provide a few densely labelled support videos, we propose to automatically learn to find appropriate support videos given a query. We call this self-shot learning and we outline a simple self-supervised learning method to generate an embedding space well-suited for unsupervised retrieval of relevant samples. To showcase this novel setting, we tackle, for the first time, video instance segmentation in a self-shot (and few-shot) setting, where the goal is to segment instances at the pixel-level across the spatial and temporal domains. We provide strong baseline performances that utilize a novel transformer-based model and show that self-shot learning can even surpass few-shot and can be positively combined for further performance gains. Experiments on new benchmarks show that our approach achieves strong performance, is competitive to oracle support in some settings, scales to large unlabelled video collections, and can be combined in a semi-supervised setting.Comment: 25 pages, 5 figures, 13 table

Benchmarking of Embedded Object Detection in Optical and RADAR Scenes

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue. The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. We have shown fast and accurate detection and tracking using synthetic and real RADAR data. Another major contribution is the quantification of the relationship between neural network mAP performance and data augmentations. As an example, we focused on image and video compression methods, such as JPEG, WebP, H264, and H265. The results show WebP at a quantization level of 50 and H265 at a constant rate factor of 30 provide the best balance between compression and acceptable mAP. Other minor contributions are achieved in enhancing the functionality of the real-time prototype system. This includes the implementation and benchmarking of neural network op- timizations, such as quantization and pruning. Furthermore, an appearance-based synthetic RADAR and real RADAR datasets are developed. The latter contains simultaneous optical and RADAR data capture and cross-modal labels. Finally, multi-object tracking methods are benchmarked and a support vector machine is utilized for cross-modal association. In summary, the implementation, benchmarking, and optimization of methods for detection and tracking helped create a real-time vital sign system on a low-profile embedded device. Additionally, this work established a relationship between compression methods and different neural networks for optimal file compression and network performance. Finally, methods for RADAR and optical data collection and cross-modal association are implemented

이야기형 설명문을 활용한 대규모 비디오 학습 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 김건희.Extensive contributions are being made to develop intelligent agents that can recognize and communicate with the world. In this sense, various video-language tasks have drawn a lot of interests in computer vision research, including image/video captioning, video retrieval and video question answering. It can be applied to high-level computer vision tasks and various future industries such as search engines, social marketing, automated driving, and robotics support through QA / dialog generation for the surrounding environment. However, despite these developments, video-language learning suffers from a higher degree of complexity. This thesis investigates methodologies for learning the relationship between videos and free-formed languages, including explanations, conversations, and question-and-answers, so that the machine can easily adapt to target downstream tasks. First, we introduce several methods to learn the relationship between long sentences and videos efficiently. We introduce the approaches for supervising human attention transfer for the video attention model, which shows the video attention mechanism can benefit from explicit human gaze labels. Next, we introduce the end-to-end semantic attention method, which further reduces the visual attention algorithm's complexity by using the representative visual concept word detected by the attention-based detector. As a follow-up study on previous methods, we introduce a JSFusion (Joint Sequence Fusion) method that enables efficient video search and QA by enabling many-to-many matching of attention model. Next, we introduce the CiSIN(Character in Story Identification Network), which uses Attention to increase the performance of character grounding and character re-identification in the movie. Finally, we introduce Transitional Adaptation, which promotes the caption generation models to generates coherent narratives for long videos. In summary, this thesis presents a novel approaches for automatic video description generation/retrieval and shows the benefits of extracting linguistic knowledge for object and motion in the video as well as the advantage of multimodal audio-visual learning for understanding videos. Since the proposed methods are easily adapted to any video-language tasks, it is expected to be applied to the latest models, bringing additional performance improvements. Moving forward, we plan to design an unsupervised video learning framework that can solve many challenges in the industry by integrating an unlimited amount of video, audio, and free-formed language data from the web.시각-언어 학습은 이미지/비디오 캡션(Image/Video captioning), 시각 질의응답(Visual Question and Answering), 비디오 검색(Video Retrieval), 장면 이해(scene understanding), 이벤트 인식(event detection) 등 고차원의 컴퓨터 비전 태스크(task)뿐만 아니라 주변 환경에 대한 질의 응답 및 대화 생성(Dialogue Generation)으로 인터넷 검색 뿐만 아니라 최근 활발한 소셜 마케팅(Social Marketing) 자율 주행(Automated Driving), 로보틱스(Robotics)을 보조하는 등 여러 미래 산업에 적용될 수 있어 활발히 연구되고 있는 중요한 분야이다. 컴퓨터 비젼과 자연어 처리는 이러한 중요성을 바탕으로 각자 고유한 영역에서 발전을 거듭해 왔으나, 최근 딥러닝의 등장과 함께 눈부시게 발전하면서 서로를 보완하며 학습 결과를 향상시키는 등 큰 시너지 효과를 발휘하게 되었다. 하지만 이런 발전에도 불구하고, 비디오-언어간 학습은 문제의 복잡도가 한층 높아 어려움을 겪게 되는 경우가 많다. 본 논문에서는 비디오와 이에 대응하는 설명, 대화, 질의 응답 등 더 나아가 자유 형태의 언어 (Free-formed language)간의 관계를 더욱 효율적으로 학습하고, 목표 임무에 잘 대응할 수 있도록 개선하는 것을 목표로 한다. 먼저, 시각적 복잡도가 이미지보다 높은 비디오와 긴 문장 사이의 관계를 효율적으로 학습하기 위한 여러 방법들을 소개한다. 인간의 주의 인식(Attention) 모델을 비디오-언어 모델에 지도 학습 하는 방법을 소개하고, 이어서 비디오에서 우선 검출된 대표 시각 단어를 매개로 하여 주의 인식(Attention) 알고리즘의 복잡도를 더욱 줄이는 의미 중심 주의 인식 (Semantic Attention) 방법, 어텐션 모델의 다대다 매칭을 기반으로 효율적인 비디오 검색 및 질의응답을 가능케 하는 비디오-언어간 융합 (Joint Sequence Fusion) 방법 등 비디오 주의 인식을 효율적으로 학습시킬 수 있는 방법들을 제시한다. 다음으로는, 주의 인식(Attention) 모델이 물체-단어 간 관계를 넘어 비디오 상에서 인물 검색 (Person Searching) 그리고 인물 재 식별 (Person Re-Identification)을 동시에 수행하며 상승작용을 일으키는 스토리 속 캐릭터 인식 신경망 (Character in Story Identification Network) 을 소개하며, 마지막으로 자기 지도 학습(Self-supervised Learning)을 통해 주의 인식(Attention) 기반 언어 모델이 긴 비디오에 대한 설명을 연관성 있게 잘 생성할 수 있도록 유도하는 방법을 소개한다. 요약하자면, 이 학위 논문에서 제안한 새로운 방법론들은 비디오-언어 학습에 해당하는 비디오 캡션(Video captioning), 비디오 검색(Video Retrieval), 시각 질의응답(Video Question and Answering)등을 해결할 수 있는 기술적 디딤돌이 되며, 비디오 캡션 학습을 통해 학습된 주의 인식 모듈은 검색 및 질의응답, 인물 검색 등 각 네트워크에 이식되면서 새로운 문제들에 대해 동시에 최고 수준(State-of-the-art)의 성능을 달성하였다. 이를 통해 비디오-언어 학습으로 얻은 언어 지식의 이전은 시각-청각을 아우르는 비디오 멀티모달 학습에 큰 도움이 되는 것을 실험적으로 보여준다. 향후 작업 방향 (Future Work)으로는 앞서 연구한 내용들을 기반으로 웹 속에 존재하는 대규모의 언어, 비디오, 오디오 데이터를 통합해 학습에 활용하여 산업계의 많은 난제를 해결할 수 있는 비지도 학습 모델을 만들고자 한다.Chapter 1 Introduction 1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 1.2 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . .8 Chapter 2 Related Work 2.1 Video Captioning . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.2 Video Retrieval with Natural Language . . . . . . . . . . . . . . 12 2.3 Video Question and Answering . . . . . . . . . . . . . . . . . . . 13 2.4 Cross-modal Representation Learning for Vision and LanguageTasks . . . . 15 Chapter 3 Human Attention Transfer for Video Captioning18 3.1 Introduction 3.2 Video Datasets for Caption and Gaze . . . . . . . . . . . . . . . 21 3.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.1 Video Pre-processing and Description . . . . . . . . . . . 22 3.3.2The Recurrent Gaze Prediction (RGP) Model . . . . . . . 23 3.3.3Construction of Visual Feature Pools . . . . . . . . . . . . 24 3.3.4The Decoder for Caption Generation . . . . . . . . . . . . 26 3.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4.1Evaluation of Gaze Prediction . . . . . . . . . . . . . . . . 29 3.4.2Evaluation of Video Captioning . . . . . . . . . . . . . . . 32 3.4.3Human Evaluation via AMT . . . . . . . . . . . . . . . . 35 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Chapter 4 Semantic Word Attention for Video QA and VideoCaptioning 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.1Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.1.2Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.2An Attention Model for Concept Detection . . . . . . . . 42 4.2.3Video-to-Language Models . . . . . . . . . . . . . . . . . 45 4.2.4A Model for Description . . . . . . . . . . . . . . . . . . . 45 4.2.5A Model for Fill-in-the-Blank . . . . . . . . . . . . . . . . 48 4.2.6A Model for Multiple-Choice Test . . . . . . . . . . . . . 50 4.2.7A Model for Retrieval . . . . . . . . . . . . . . . . . . . . 51 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.1The LSMDC Dataset and Tasks . . . . . . . . . . . . . . 52 4.3.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 54 4.3.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 56 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter 5 Joint Sequnece Fusion Attention for Multimodal Sequence Data 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.1Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.2The Joint Semantic Tensor . . . . . . . . . . . . . . . . . 65 5.3.3The Convolutional Hierarchical Decoder . . . . . . . . . . 66 5.3.4An Illustrative Example of How the JSFusion Model Works 68 5.3.5Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.3.6Implementation of Video-Language Models . . . . . . . . 69 5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.4.1LSMDC Dataset and Tasks . . . . . . . . . . . . . . . . . 71 5.4.2MSR-VTT-(RET/MC) Dataset and Tasks . . . . . . . . . 73 5.4.3Quantitative Results . . . . . . . . . . . . . . . . . . . . . 74 5.4.4Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 76 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Chapter 6 Character Re-Identification and Character Ground-ing for Movie Understanding 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.3.1Video Preprocessing . . . . . . . . . . . . . . . . . . . . . 84 6.3.2Visual Track Embedding . . . . . . . . . . . . . . . . . . . 85 6.3.3Textual Character Embedding . . . . . . . . . . . . . . . 86 6.3.4Character Grounding . . . . . . . . . . . . . . . . . . . . 87 6.3.5Re-Identification . . . . . . . . . . . . . . . . . . . . . . . 88 6.3.6Joint Training . . . . . . . . . . . . . . . . . . . . . . . . 90 6.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 92 6.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 93 6.4.3Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 95 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Chapter 7 Transitional Adaptation of Pretrained Models forVisual Storytelling 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.3.1The Visual Encoder . . . . . . . . . . . . . . . . . . . . . 104 7.3.2The Language Generator . . . . . . . . . . . . . . . . . . 104 7.3.3Adaptation training . . . . . . . . . . . . . . . . . . . . . 105 7.3.4The Sequential Coherence Loss . . . . . . . . . . . . . . . 105 7.3.5Training with the adaptation Loss . . . . . . . . . . . . . 107 7.3.6Fine-tuning and Inference . . . . . . . . . . . . . . . . . . 107 7.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 7.4.1Experimental Setup . . . . . . . . . . . . . . . . . . . . . 109 7.4.2Quantitative Results . . . . . . . . . . . . . . . . . . . . . 112 7.4.3Further Analyses . . . . . . . . . . . . . . . . . . . . . . . 112 7.4.4Human Evaluation Results . . . . . . . . . . . . . . . . . 115 7.4.5Qualitative Results . . . . . . . . . . . . . . . . . . . . . . 116 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Chapter 8 Conclusion 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 8.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 Bibliography ... 123 요약 ... 148 Acknowledgements ... 150Docto

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Currently, there are computer vision systems that help us with tasks that would be dull for humans, such as surveillance and vehicle tracking. An important part of this analysis is to identify traffic anomalies. An anomaly tells us that something unusual has happened, in this case on the highway. This paper aims to model vehicle tracking using computer vision to detect traffic anomalies on a highway. We develop the steps of detection, tracking, and analysis of traffic: the detection of vehicles from video of urban traffic, the tracking of vehicles using a bipartite graph and the Convex Hull algorithm to delimit moving areas. Finally for anomaly detection we use two data structures to detect the beginning and end of the anomaly. The first is the QuadTree that groups vehicles that are stopped for a long time on the road and the second that approaches vehicles that are occluded. Experimental results show that our method is acceptable on the Track4 test set, with an F1 score of 85.7% and a mean squared error of 25.432.Comment: 14 pages, 14 figures, submitted to Journal of Internet Services and Applications - JIS

Analysis of Using Metric Access Methods for Visual Search of Objects in Video Databases

This article presents an approach to object retrieval that searches for and localizes all the occurrences of an object in a video database, given a query image of the object. Our proposal is based on text-retrieval methods in which video key frames are represented by a dense set of viewpoint invariant region descriptors that enable recognition to proceed successfully despite changes in camera viewpoint, lighting, and partial occlusions. Vector quantizing these region descriptors provides a visual analogy of a word - a visual word. Those words are grouped into a visual vocabulary which is used to index all key frames from the video database. Efficient retrieval is then achieved by employing methods from statistical text retrieval, including inverted file systems, and text-document frequency weightings. Though works in the literature have only adopted a simple sequential scan during search, we investigate the use of different metric access methods (MAM): M-tree, Slim-tree, and D-index, in order to accelerate the processing of similarity queries. In addition, a ranking strategy based on the spatial layout of the regions (spatial consistency) is fully described and evaluated. Experimental results have shown that the adoption of MAMs not only has improved the search performance but also has reduced the influence of the vocabulary size over test results, which may improve the scalability of our proposal. Finally, the application of spatial consistency has produced a very significant improvement of the results

Probabilistic Face Tracking From Location and Facial Identity Information

In recent times, the advancement in object detection has induced new video processing applications. This paper explores the development of the face tracking system with a probabilistic approach where face similarities and relative positions are considered. The potential applications and further enhancement includes detecting the human faces along with the behavior analysis and video surveillance at the chaotic situation such as objects overlapping. The system includes three stages - face detection and face alignment using MTCNN, face recognition using FaceNet, and face tracking after Gibbs sampling. I evaluate the full-temporal method and three other approaches - baseline, positional, and instantaneous methods