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Works197 from public data
- Ego4D: Around the World in 3,000 Hours of Egocentric Video2,124
Ego4D, a massive-scale egocentric video dataset and benchmark suite, is introduced and a host of new benchmark challenges centered around understanding the first-person visual experience in the past, present, and future are presented.
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- EPIC-KITCHENS-100806
The pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS, using a novel pipeline that allows denser and more complete annotations of fine-grained actions and enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later.
- EPIC-KITCHENS-100806
The pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS, using a novel pipeline that allows denser and more complete annotations of fine-grained actions and enables new challenges such as action detection and evaluating the “test of time”—i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later.
- Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives640
To push the frontier of first-person video understanding of skilled human activity, a suite of benchmark tasks and their annotations are presented, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose.
- Scaling Egocentric Vision: The "Equation missing" Dataset539
This paper introduces Open image in new window, a large-scale egocentric video benchmark recorded by 32 participants in their native kitchen environments and had the participants narrate their own videos (after recording), thus reflecting true intention, and crowd-sourced ground-truths based on these.
- EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition409
This work proposes a novel architecture for multi-modal temporal-binding, i.e. the combination of modalities within a range of temporal offsets, and demonstrates the importance of audio in egocentric vision, on per-class basis, for identifying actions as well as interacting objects.
- The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines402
This paper details how this large-scale dataset was captured by 32 participants in their native kitchen environments, and densely annotated with actions and object interactions, and introduces new baselines that highlight the multimodal nature of the dataset and the importance of explicit temporal modelling to discriminate fine-grained actions.
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- Detecting Carried Objects in Short Video Sequences245
A new method for detecting objects such as bags carried by pedestrians depicted in short video sequences by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians, which yields a segmentation of carried objects using the MAP solution.
- Multi-Modal Domain Adaptation for Fine-Grained Action Recognition241
This work exploits the correspondence of modalities as a self-supervised alignment approach for UDA in addition to adversarial alignment to exploit the multi-modal nature of video within each domain.
- Temporal-Relational CrossTransformers for Few-Shot Action Recognition200
This work proposes a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set, and outperforms prior work on SSv2 by a wide margin due to the its ability to model temporal relations.
- Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings191
This paper proposes to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions by building a separate multi-modal embedding space for each PoS tag, which enables learning specialised embedding spaces that offer multiple views of the same embedded entities.
- The Pros and Cons: Rank-Aware Temporal Attention for Skill Determination in Long Videos178
A new model to determine relative skill from long videos, through learnable temporal attention modules, using a novel rank-aware loss function that outperforms previous approaches and classic softmax attention on both datasets by over 4% pairwise accuracy, and as much as 12% on individual tasks.
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- You-Do, I-Learn: Discovering Task Relevant Objects and their Modes of Interaction from Multi-User Egocentric Video166
This work presents a fully unsupervised approach for the discovery of i) task relevant objects and ii) how these objects have been used and introduces the term Mode of Interaction (MOI) to refer to the different ways in which TROs are used.
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- An Outlook into the Future of Egocentric Vision121
The gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in the authors' every day lives is explored.
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- SPHERE: A Sensor Platform for Healthcare in a Residential Environment88
The home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world and broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery.
- On Semantic Similarity in Video Retrieval85
This paper proposes several proxies to estimate semantic similarities in large-scale retrieval datasets, without additional annotations, and proposes a move to semantic similarity video retrieval, where multiple videos/captions can be deemed equally relevant, and their relative ranking does not affect a method’s reported performance.
- Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling85
A real-time RGB-D object tracker which manages occlusions and scale changes in a wide variety of scenarios and matches, and in many cases outperforms, state-of-the-art algorithms for precision and it far exceeds most in speed.
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 2026-10-12. 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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