A Human-ML Collaboration Framework for Improving Video Content Reviews

• URL: http://arxiv.org/abs/2210.09500v1
• Date: Tue, 18 Oct 2022 00:47:16 GMT
• Title: A Human-ML Collaboration Framework for Improving Video Content Reviews
• Authors: Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen
• Abstract summary: We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain. Our key contribution is a novel human-machine learning (ML) collaboration framework aimed at maximizing the quality and efficiency of human decisions.
• Score: 12.841825384884679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We deal with the problem of localized in-video taxonomic human annotation in the video content moderation domain, where the goal is to identify video segments that violate granular policies, e.g., community guidelines on an online video platform. High quality human labeling is critical for enforcement in content moderation. This is challenging due to the problem of information overload - raters need to apply a large taxonomy of granular policy violations with ambiguous definitions, within a limited review duration to relatively long videos. Our key contribution is a novel human-machine learning (ML) collaboration framework aimed at maximizing the quality and efficiency of human decisions in this setting - human labels are used to train segment-level models, the predictions of which are displayed as "hints" to human raters, indicating probable regions of the video with specific policy violations. The human verified/corrected segment labels can help refine the model further, hence creating a human-ML positive feedback loop. Experiments show improved human video moderation decision quality, and efficiency through more granular annotations submitted within a similar review duration, which enable a 5-8% AUC improvement in the hint generation models.

Related papers

• HV-MMBench: Benchmarking MLLMs for Human-Centric Video Understanding [79.06209664703258]
  Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos.<n>Existing human-centric benchmarks predominantly emphasize video generation quality and action recognition, while overlooking essential perceptual and cognitive abilities required in human-centered scenarios.<n>We propose a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric video understanding.
  arXiv  Detail & Related papers  (2025-07-07T11:52:24Z)
• AVC-DPO: Aligned Video Captioning via Direct Preference Optimization [50.08618093204503]
  Video multimodal large language models (video MLLMs) have achieved substantial progress in video captioning tasks.<n>We propose Aligned Video Captioning via Direct Preference Optimization (AVC-DPO), a post-training framework designed to enhance captioning capabilities in video MLLMs through preference alignment.<n>We have achieved exceptional performance in the LOVE@PRCV'25 Workshop Track 1A: Video Detailed Captioning Challenge, achieving first place on the Video Detailed Captioning benchmark.
  arXiv  Detail & Related papers  (2025-07-02T08:51:45Z)
• Robustness Evaluation for Video Models with Reinforcement Learning [4.0196072781228285]
  We propose a multi-agent reinforcement learning approach that learns cooperatively to identify the given video's sensitive spatial and temporal regions.<n>Our method outperforms the state-of-the-art solutions on the Lp metric and the average queries.
  arXiv  Detail & Related papers  (2025-06-05T08:38:09Z)
• HumanVBench: Exploring Human-Centric Video Understanding Capabilities of MLLMs with Synthetic Benchmark Data [55.739633494946204]
  We present HumanVBench, an innovative benchmark meticulously crafted to bridge gaps in the evaluation of video MLLMs. HumanVBench comprises 16 carefully designed tasks that explore two primary dimensions: inner emotion and outer manifestations, spanning static and dynamic, basic and complex, as well as single-modal and cross-modal aspects. A comprehensive evaluation across 22 SOTA video MLLMs reveals notable limitations in current performance, especially in cross-modal and emotion perception.
  arXiv  Detail & Related papers  (2024-12-23T13:45:56Z)
• OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation [27.516068877910254]
  We introduce OpenHumanVid, a large-scale and high-quality human-centric video dataset. Our findings yield two critical insights: First, the incorporation of a large-scale, high-quality dataset substantially enhances evaluation metrics for generated human videos. Second, the effective alignment of text with human appearance, human motion, and facial motion is essential for producing high-quality video outputs.
  arXiv  Detail & Related papers  (2024-11-28T07:01:06Z)
• FIOVA: A Multi-Annotator Benchmark for Human-Aligned Video Captioning [15.363132825156477]
  We introduce FIOVA, a human-centric benchmark tailored for evaluation of large vision-language models (LVLMs)<n>It comprises 3,002 real-world videos (about 33.6s each), each annotated independently by five annotators.<n>We propose FIOVA-DQ, an event-level evaluation metric that incorporates cognitive weights derived from annotator consensus.
  arXiv  Detail & Related papers  (2024-10-20T03:59:54Z)
• CinePile: A Long Video Question Answering Dataset and Benchmark [55.30860239555001]
  We present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding. Our comprehensive dataset comprises 305,000 multiple-choice questions (MCQs), covering various visual and multimodal aspects. We fine-tuned open-source Video-LLMs on the training split and evaluated both open-source and proprietary video-centric LLMs on the test split of our dataset.
  arXiv  Detail & Related papers  (2024-05-14T17:59:02Z)
• InstructVideo: Instructing Video Diffusion Models with Human Feedback [65.9590462317474]
  We propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain, we recast reward fine-tuning as editing.
  arXiv  Detail & Related papers  (2023-12-19T17:55:16Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Conditional Modeling Based Automatic Video Summarization [70.96973928590958]
  The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story. Video summarization methods rely on visual factors, such as visual consecutiveness and diversity, which may not be sufficient to fully understand the content of the video. A new approach to video summarization is proposed based on insights gained from how humans create ground truth video summaries.
  arXiv  Detail & Related papers  (2023-11-20T20:24:45Z)
• EvalCrafter: Benchmarking and Evaluating Large Video Generation Models [70.19437817951673]
  We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Our approach involves generating a diverse and comprehensive list of 700 prompts for text-to-video generation. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmark, in terms of visual qualities, content qualities, motion qualities, and text-video alignment with 17 well-selected objective metrics.
  arXiv  Detail & Related papers  (2023-10-17T17:50:46Z)
• Video Summarization Based on Video-text Modelling [0.0]
  We propose a multimodal self-supervised learning framework to obtain semantic representations of videos. We also introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. An objective evaluation framework is proposed to measure the quality of video summaries based on video classification.
  arXiv  Detail & Related papers  (2022-01-07T15:21:46Z)
• Use of Affective Visual Information for Summarization of Human-Centric Videos [13.273989782771556]
  We investigate the affective-information enriched supervised video summarization task for human-centric videos. First, we train a visual input-driven state-of-the-art continuous emotion recognition model (CER-NET) on the RECOLA dataset to estimate emotional attributes. Then, we integrate the estimated emotional attributes and the high-level representations from the CER-NET with the visual information to define the proposed affective video summarization architectures (AVSUM)
  arXiv  Detail & Related papers  (2021-07-08T11:46:04Z)
• On Development and Evaluation of Retargeting Human Motion and Appearance in Monocular Videos [2.870762512009438]
  Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision. We propose a novel and high-performant approach based on a hybrid image-based rendering technique that exhibits competitive visual quality. We also present a new video benchmark dataset composed of different videos with annotated human motions to evaluate the task of synthesizing people's videos.
  arXiv  Detail & Related papers  (2021-03-29T13:17:41Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.