Search Results for author:
Found 28 papers, 11 papers with code
Gen2Det: Generate to Detect
In the long-tailed detection setting on LVIS, Gen2Det improves the performance on rare categories by a large margin while also significantly improving the performance on other categories, e. g. we see an improvement of 2. 13 Box AP and 1. 84 Mask AP over just training on real data on LVIS with Mask R-CNN.
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
We present a new framework leveraging off-the-shelf generative models to generate synthetic training images, addressing multiple challenges: class name ambiguity, lack of diversity in naive prompts, and domain shifts.
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e. g., ~4 AP on COCO/LVIS) over other fast SAM models.
Ranked #3 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
To bootstrap the research on EgoObjects, we present a suite of 4 benchmark tasks around the egocentric object understanding, including a novel instance level- and the classical category level object detection.
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Although acquired extensive knowledge of visual concepts, it is non-trivial to exploit knowledge from these VL models to the task of semantic segmentation, as they are usually trained at an image level.
Going Denser with Open-Vocabulary Part Segmentation
In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation.
Exploring Open-Vocabulary Semantic Segmentation from CLIP Vision Encoder Distillation Only
Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level.
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community.
SCVRL: Shuffled Contrastive Video Representation Learning
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos.
Hierarchical Self-supervised Representation Learning for Movie Understanding
Most self-supervised video representation learning approaches focus on action recognition.
MaCLR: Motion-aware Contrastive Learning of Representations for Videos
We present MaCLR, a novel method to explicitly perform cross-modal self-supervised video representations learning from visual and motion modalities.
YolactEdge: Real-time Instance Segmentation on the Edge
We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model
Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.
Delving Deeper into Anti-aliasing in ConvNets
Aliasing refers to the phenomenon that high frequency signals degenerate into completely different ones after sampling.
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
Audiovisual SlowFast Networks for Video Recognition
We present Audiovisual SlowFast Networks, an architecture for integrated audiovisual perception.
YOLACT++: Better Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #15 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos
We propose a novel approach that disentangles the identity and pose of objects for image generation.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
Ranked #7 on
Action Detection
on UCF101-24
YOLACT: Real-time Instance Segmentation
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #21 on
Real-time Instance Segmentation
on MSCOCO
(using extra training data)
Video Object Detection with an Aligned Spatial-Temporal Memory
We introduce Spatial-Temporal Memory Networks for video object detection.
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest.
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i. e., localize) arbitrary linguistic phrases, in the form of spatial attention masks.
Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals
We present an unsupervised approach that generates a diverse, ranked set of bounding box and segmentation video object proposals---spatio-temporal tubes that localize the foreground objects---in an unannotated video.
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
The status quo approach to training object detectors requires expensive bounding box annotations.
Discovering the Spatial Extent of Relative Attributes
We present a weakly-supervised approach that discovers the spatial extent of relative attributes, given only pairs of ordered images.
Transitive Distance Clustering with K-Means Duality
We propose a very intuitive and simple approximation for the conventional spectral clustering methods.
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers
In this paper we propose a multi-task linear classifier learning problem called D-SVM (Dictionary SVM).
