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Works42 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.
- 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.
- 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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- 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.
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- 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.
- GenHowTo: Learning to Generate Actions and State Transformations from Instructional Videos59
This work addresses the task of generating temporally consistent and physically plausible images of actions and object state transformations and develops and train a conditioned diffusion model dubbed GenHowTo, which achieves superior performance compared to existing methods.
- Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video35
It is demonstrated that disagreement stems from a limited understanding of the distinct phases of an action, and proposed annotating based on the Rubicon Boundaries, inspired by a similarly named cognitive model, for consistent temporal bounds of object interactions is proposed.
- SEMBED: Semantic Embedding of Egocentric Action Videos18
This work presents SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic labels, and shows how it can interpret a challenging dataset of 1225 freely annotated Egocentric videos.
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- Ego4D: Around the World in 3,600 Hours of Egocentric Video15
Ego4D, a massive-scale egocentric video dataset and benchmark suite, introduced, dramatically expands the volume of diverse egocentric video footage publicly available to the research community and presents a host of new benchmark challenges centered around understanding the first-person visual experience.
- ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions14
This work introduces an automatic approach for collecting large step-by-step visual instruction training data from instructional videos and develops and trains ShowHowTo, a video diffusion model capable of generating step-by-step visual instructions consistent with the provided input image.
- HOI-Ref: Hand-Object Interaction Referral in Egocentric Vision12
The results demonstrate that VLMs trained for referral on third person images fail to recognise and refer hands and objects in egocentric images, and when fine-tuned on the egocentric HOI-QA dataset, performance improves.
- Moment of Untruth: Dealing with Negative Queries in Video Moment Retrieval11
The ability of current SOTA video moment retrieval approaches to adapt to Negative-Aware Video Moment Retrieval is analysed and UniVTG-NA, an adaptation of UniVTG designed to tackle NA-VMR is proposed.
- Egocentric Video-Language Pretraining @ Ego4D Challenge 20228
A pretrained video-language model that is able to transfer its egocentric video-text representation or video-only representation to several video downstream tasks and pioneer Egocentric VLP from pretraining dataset, pretraining objective, and development set is developed.
- Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videos7
The first struggle dataset with three real-world problem-solving activities that are labelled by both expert and crowd-source annotators is introduced, indicating that struggle detection in video can achieve up to 88.24% accuracy in binary classification, while detecting the level of struggle in a four-way classification setting performs lower.
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- The Cage: Towards a 6-DoF Remote Control with Force Feedback for UAV Interaction5
This work presents The Cage, a 6-DoF controller for piloting an unmanned aerial vehicle (UAV), comprised of a handle suspended by eight lines, each coupled to a vertex, within a physical cuboid cage.
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- Learning Temporal Sentence Grounding From Narrated EgoVideos3
This paper proposes to artificially merge clips to train for temporal grounding in a contrastive manner using text-conditioning attention and this Clip Merging (CliMer) approach is shown to be effective when compared with a high performing TSG method.
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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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