283 citations · 295 across the 21 of their papers we have counts for
25 papers
Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
Luc P. J. Sträter, Hazel Doughty
Parameter-efficient fine-tuning (PEFT) and probing enable adaptation of foundation models using only a small number of trainable parameters, making it attractive for video understa…
Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding
Kaiting Liu, Hazel Doughty
Video recognition models are typically trained on fixed taxonomies which are often too coarse, collapsing distinctions in object, manner or outcome under a single label. As tasks a…
Segmenting Collision Sound Sources in Egocentric Videos
Kranti Kumar Parida, Omar Emara, Hazel Doughty +1
Humans excel at multisensory perception and can often recognise object properties from the sound of their interactions. Inspired by this, we propose the novel task of Collision Sou…
SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning
Fida Mohammad Thoker, Letian Jiang, Chen Zhao +4
Continued advances in self-supervised learning have led to significant progress in video representation learning, offering a scalable alternative to supervised approaches by removi…
HD-EPIC: A Highly-Detailed Egocentric Video Dataset
Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha +16
We present a validation dataset of newly-collected kitchen-based egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe…
LocoMotion: Learning Motion-Focused Video-Language Representations
Hazel Doughty, Fida Mohammad Thoker, Cees G. M. Snoek
This paper strives for motion-focused video-language representations. Existing methods to learn video-language representations use spatial-focused data, where identifying the objec…
Beyond Coarse-Grained Matching in Video-Text Retrieval
Aozhu Chen, Hazel Doughty, Xirong Li +1
Video-text retrieval has seen significant advancements, yet the ability of models to discern subtle differences in captions still requires verification. In this paper, we introduce…
SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery
Sarah Rastegar, Mohammadreza Salehi, Yuki M. Asano +2
In this paper, we address Generalized Category Discovery, aiming to simultaneously uncover novel categories and accurately classify known ones. Traditional methods, which lean heav…
Low-Resource Vision Challenges for Foundation Models
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are…
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek
In the quest for unveiling novel categories at test time, we confront the inherent limitations of traditional supervised recognition models that are restricted by a predefined cate…
Learning Unseen Modality Interaction
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-comp…
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
Fida Mohammad Thoker, Hazel Doughty, Cees Snoek
We propose a self-supervised method for learning motion-focused video representations. Existing approaches minimize distances between temporally augmented videos, which maintain hi…
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight
Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
This paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not t…
Audio-Adaptive Activity Recognition Across Video Domains
Yunhua Zhang, Hazel Doughty, Ling Shao +1
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint. The leading approaches reduce the shift in activity app…
How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
Fida Mohammad Thoker, Hazel Doughty, Piyush Bagad +1
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability. In this paper, we investigate how s…
How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs
Hazel Doughty, Cees G. M. Snoek
We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a method which recognizes adverbs a…
Skeleton-Contrastive 3D Action Representation Learning
Fida Mohammad Thoker, Hazel Doughty, Cees G. M. Snoek
This paper strives for self-supervised learning of a feature space suitable for skeleton-based action recognition. Our proposal is built upon learning invariances to input skeleton…
On Semantic Similarity in Video Retrieval
Michael Wray, Hazel Doughty, Dima Damen
Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. We demonstrate t…
WiCV 2020: The Seventh Women In Computer Vision Workshop
Hazel Doughty, Nour Karessli, Kathryn Leonard +5
In this paper we present the details of Women in Computer Vision Workshop - WiCV 2020, organized in alongside virtual CVPR 2020. This event aims at encouraging the women researcher…
Rescaling Egocentric Vision
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M fra…
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, t…
Action Modifiers: Learning from Adverbs in Instructional Videos
Hazel Doughty, Ivan Laptev, Walterio Mayol-Cuevas +1
We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the…
The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos
Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
We present a new model to determine relative skill from long videos, through learnable temporal attention modules. Skill determination is formulated as a ranking problem, making it…
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
Dima Damen, Hazel Doughty, Giovanni Maria Farinella +8
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention. However, progress in this challen…