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Works237 from public data
- 4,230
- On Space-Time Interest Points4,065
This paper builds on the idea of the Harris and Förstner interest point operators and detects local structures in space-time where the image values have significant local variations in both space and time and illustrates how a video representation in terms of local space- time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.
- 3,819
- 3,316
- Segmenter: Transformer for Semantic Segmentation2,051
This paper introduces Segmenter, a transformer model for semantic segmentation that outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes.
- HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips1,545
It is demonstrated that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask.
- Evaluation of local spatio-temporal features for action recognition1,508
It is demonstrated that regular sampling of space-time features consistently outperforms all testedspace-time interest point detectors for human actions in realistic settings and is a consistent ranking for the majority of methods over different datasets.
- Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding1,455
This work proposes a novel Hollywood in Homes approach to collect data, collecting a new dataset, Charades, with hundreds of people recording videos in their own homes, acting out casual everyday activities, and evaluates and provides baseline results for several tasks including action recognition and automatic description generation.
- 1,407
- Learning from Synthetic Humans1,076
This work presents SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data and shows that CNNs trained on this synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images.
- Long-Term Temporal Convolutions for Action Recognition954
It is demonstrated that LTC-CNN models with increased temporal extents improve the accuracy of action recognition and the impact of different low-level representations, such as raw values of video pixels and optical flow vector fields, and the importance of high-quality optical flow estimation for learning accurate action models.
- 938
- The THUMOS challenge on action recognition for videos “in the wild”918
The THUMOS benchmark is described in detail, the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal action detection are presented, and how well methods trained on trimmed videos generalize to untrimmed videos is evaluated.
- End-to-End Learning of Visual Representations From Uncurated Instructional Videos813
This work proposes a new learning approach, MIL-NCE, capable of addressing mis- alignments inherent in narrated videos and outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines.
- Learning Joint Reconstruction of Hands and Manipulated Objects683
This work presents an end-to-end learnable model that exploits a novel contact loss that favors phys- ically plausible hand-object constellations, and improves grasp quality metrics over baselines, using RGB images as input.
- P-CNN: Pose-Based CNN Features for Action Recognition641
A new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition that aggregates motion and appearance information along tracks of human body parts is proposed.
- 511
- BodyNet: Volumetric Inference of 3D Human Body Shapes457
BodyNet is an end-to-end trainable network that benefits from a volumetric 3D loss, a multi-view re-projection loss, and intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose and achieves state-of-the-art performance.
- Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning427
Vid2Seq is introduced, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale and improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions.
- 392
- 381
- 378
- Just Ask: Learning to Answer Questions from Millions of Narrated Videos360
This work proposes to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision, and introduces iVQA, a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations.
- Cross-Task Weakly Supervised Learning From Instructional Videos330
The experiments demonstrate that sharing across tasks improves performance, especially when done at the component level and that the component model can parse previously unseen tasks by virtue of its compositionality.
- Unsupervised Learning from Narrated Instruction Videos329
A new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration to solve two clustering problems, one in text and one in video, and can automatically discover the main steps to achieve the task and locate the steps in the input videos.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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