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Found 22 papers, 7 papers with code
One-Shot Open Affordance Learning with Foundation Models
We introduce One-shot Open Affordance Learning (OOAL), where a model is trained with just one example per base object category, but is expected to identify novel objects and affordances.
Efficient Pre-training for Localized Instruction Generation of Videos
Understanding such videos is challenging, involving the precise localization of steps and the generation of textual instructions.
Watt For What: Rethinking Deep Learning's Energy-Performance Relationship
Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy.
Telling Stories for Common Sense Zero-Shot Action Recognition
The textual narratives forge connections between seen and unseen classes, overcoming the bottleneck of labeled data that has long impeded advancements in this exciting domain.
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
The goal of this work is to understand the way actions are performed in videos.
Ranked #2 on
Video-Adverb Retrieval
on HowTo100M Adverbs
LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding
A key step to acquire this skill is to identify what part of the object affords each action, which is called affordance grounding.
An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition
In practice, a given video can contain multiple valid positive annotations for the same action.
A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos
We refer to this task as Procedure Segmentation and Summarization (PSS).
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition
We propose to learn what makes a good video for action recognition and select only high-quality samples for augmentation.
Ranked #2 on
Few Shot Action Recognition
on HMDB51
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
We address the problem of capturing temporal information for video classification in 2D networks, without increasing their computational cost.
A New Split for Evaluating True Zero-Shot Action Recognition
We benchmark several recent approaches on the proposed True Zero-Shot(TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation.
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation.
Ranked #58 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition
Theproblem can be seen as learning a function which general-izes well to instances of unseen classes without losing dis-crimination between classes.
Ranked #2 on
Zero-Shot Action Recognition
on Olympics
SMART Frame Selection for Action Recognition
In this work, however, we focus on the more standard short, trimmed action recognition problem.
Ranked #4 on
Action Recognition
on UCF101
ALBA : Reinforcement Learning for Video Object Segmentation
We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time.
Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
The workshop was held in conjunction with the International Conference on Learning Representations (ICLR) 2020.
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling
However, in current video datasets it has been observed that action classes can often be recognized without any temporal information from a single frame of video.
FASTER Recurrent Networks for Efficient Video Classification
FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities.
Ranked #26 on
Action Recognition
on UCF101
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Motion has shown to be useful for video understanding, where motion is typically represented by optical flow.
Ranked #1 on
Action Recognition
on UCF-101
On the Integration of Optical Flow and Action Recognition
Here we take a deeper look at the combination of flow and action recognition, and investigate why optical flow is helpful, what makes a flow method good for action recognition, and how we can make it better.
Optical Flow in Mostly Rigid Scenes
Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes.
Ranked #13 on
Optical Flow Estimation
on Sintel-clean
Optical Flow with Semantic Segmentation and Localized Layers
Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow.
