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Found 24 papers, 12 papers with code
SqueezeSAM: User friendly mobile interactive segmentation
Finally, when a user clicks on an object, they typically expect all related pieces of the object to be segmented.
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.
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage.
Ranked #10 on
Object Detection
on COCO 2017 val
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.
Unitail: Detecting, Reading, and Matching in Retail Scene
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene.
Ranked #1 on
Dense Object Detection
on SKU-110K
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection.
Ranked #14 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Solving Missing-Annotation Object Detection with Background Recalibration Loss
In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
Soft Anchor-Point Object Detection
In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.
Ranked #3 on
Dense Object Detection
on SKU-110K
SAPD：Soft Anchor-Point Detector
In this work, we aim at finding a new balance of speed and accuracy for anchor-free detectors.
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
Feature Selective Anchor-Free Module for Single-Shot Object Detection
The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches.
Ranked #128 on
Object Detection
on COCO test-dev
Bounding Box Regression with Uncertainty for Accurate Object Detection
Large-scale object detection datasets (e. g., MS-COCO) try to define the ground truth bounding boxes as clear as possible.
Ranked #22 on
Object Detection
on PASCAL VOC 2007
Seeing Small Faces from Robust Anchor's Perspective
This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces.
Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses
We present our novel approach to simultaneously extract the 3D shape of the face and the semantically consistent 2D alignment through a 3D Spatial Transformer Network (3DSTN) to model both the camera projection matrix and the warping parameters of a 3D model.
Ranked #10 on
Face Alignment
on AFLW2000-3D
Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation
To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under the energy minimization of a variational LS functional.
Towards a Deep Learning Framework for Unconstrained Face Detection
Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc.
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to support various facial related problems, i. e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc.
Ranked #30 on
Face Detection
on WIDER Face (Medium)
