Search Results for author:
Found 21 papers, 10 papers with code
SiLK -- Simple Learned Keypoints
Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
EgoTracks: A Long-term Egocentric Visual Object Tracking Dataset
We thus introduce EgoTracks, a new dataset for long-term egocentric visual object tracking.
Relational Space-Time Query in Long-Form Videos
In this paper, we propose to study these problems in a joint framework for long video understanding.
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity
From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO.
GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
In this paper, we introduce a new dataset called Kinetic-GEB+.
Ranked #1 on
Text to Video Retrieval
on Kinetics-GEB+
(text-to-video R@1 metric)
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.
Searching for Two-Stream Models in Multivariate Space for Video Recognition
We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models.
Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation
Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption.
Generic Event Boundary Detection: A Benchmark for Event Segmentation
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.
FP-NAS: Fast Probabilistic Neural Architecture Search
Furthermore, to search fast in the multi-variate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude.
SF-Net: Single-Frame Supervision for Temporal Action Localization
To obtain the single-frame supervision, the annotators are asked to identify only a single frame within the temporal window of an action.
Ranked #4 on
Weakly Supervised Action Localization
on BEOID
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Our key idea is to decorrelate feature representations of a category from its co-occurring context.
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 #23 on
Action Recognition
on UCF101
Video Modeling with Correlation Networks
Motion is a salient cue to recognize actions in video.
Ranked #86 on
Action Classification
on Kinetics-400
What Makes Training Multi-Modal Classification Networks Hard?
Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart.
Ranked #1 on
Action Recognition In Videos
on miniSports
Large-scale weakly-supervised pre-training for video action recognition
Second, frame-based models perform quite well on action recognition; is pre-training for good image features sufficient or is pre-training for spatio-temporal features valuable for optimal transfer learning?
Ranked #2 on
Egocentric Activity Recognition
on EPIC-KITCHENS-55
(Actions Top-1 (S2) metric)
Video Classification with Channel-Separated Convolutional Networks
It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks.
Ranked #1 on
Action Recognition
on Sports-1M
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset
The videos retrieved by the search engines are then veried for correctness by human annotators.
Latent Geometry and Memorization in Generative Models
As any generative model induces a probability density on its output domain, we propose studying this density directly.
