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Found 16 papers, 12 papers with code
Low-Resource Vision Challenges for Foundation Models
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale.
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
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 category set.
Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization
By simulating different tubelet motions and applying transformations, such as scaling and rotation, we introduce motion patterns beyond what is present in the pretraining data.
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight
The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time.
How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?
Despite the recent success of video self-supervised learning models, there is much still to be understood about their generalization capability.
Audio-Adaptive Activity Recognition Across Video Domains
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint.
How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs
We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'.
Skeleton-Contrastive 3D Action Representation Learning
In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner.
On Semantic Similarity in Video Retrieval
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.
WiCV 2020: The Seventh Women In Computer Vision Workshop
It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry.
Rescaling Egocentric Vision
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Ranked #6 on
Action Anticipation
on EPIC-KITCHENS-100
The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines
Our dataset features 55 hours of video consisting of 11. 5M frames, which we densely labelled for a total of 39. 6K action segments and 454. 2K object bounding boxes.
Action Modifiers: Learning from Adverbs in Instructional Videos
We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations.
The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos
In addition to attending to task relevant video parts, our proposed loss jointly trains two attention modules to separately attend to video parts which are indicative of higher (pros) and lower (cons) skill.
Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention.
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination
We present a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough.
