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Found 30 papers, 17 papers with code
P2Object: Single Point Supervised Object Detection and Instance Segmentation
In this paper, we introduce Point-to-Box Network (P2BNet), which constructs balanced \textbf{\textit{instance-level proposal bags}} by generating proposals in an anchor-like way and refining the proposals in a coarse-to-fine paradigm.
Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection
Accurately estimating the orientation of visual objects with compact rotated bounding boxes (RBoxes) has become a prominent demand, which challenges existing object detection paradigms that only use horizontal bounding boxes (HBoxes).
GaGA: Towards Interactive Global Geolocation Assistant
Global geolocation, which seeks to predict the geographical location of images captured anywhere in the world, is one of the most challenging tasks in the field of computer vision.
ClickTrack: Towards Real-time Interactive Single Object Tracking
The bounding box will be used as input of single object trackers.
ViMoE: An Empirical Study of Designing Vision Mixture-of-Experts
Mixture-of-Experts (MoE) models embody the divide-and-conquer concept and are a promising approach for increasing model capacity, demonstrating excellent scalability across multiple domains.
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges.
CPR++: Object Localization via Single Coarse Point Supervision
CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point.
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions.
Weakly Supervised Video Individual Counting
To provide a more realistic reflection of the underlying practical challenge we introduce a weakly supervised VIC task wherein trajectory labels are not provided.
Semantic-aware SAM for Point-Prompted Instance Segmentation
In this paper, we introduce a cost-effective category-specific segmenter using SAM.
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
% To provide a more realistic reflection of the underlying practical challenge, we introduce a weakly supervised VIC task, wherein trajectory labels are not provided.
P2RBox: Point Prompt Oriented Object Detection with SAM
In this study, we introduce P2RBox, which employs point prompt to generate rotated box (RBox) annotation for oriented object detection.
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects).
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection
In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time.
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Crowd localization aims to predict the spatial position of humans in a crowd scenario.
Multi-Attention Network for Compressed Video Referring Object Segmentation
To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module.
Ranked #5 on
Referring Expression Segmentation
on A2D Sentences
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
Object Localization under Single Coarse Point Supervision
In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.
P2P-Loc: Point to Point Tiny Person Localization
As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way.
Rethinking Sampling Strategies for Unsupervised Person Re-identification
While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
TS-CAM finally couples the patch tokens with the semantic-agnostic attention map to achieve semantic-aware localization.
SM+: Refined Scale Match for Tiny Person Detection
Detecting tiny objects ( e. g., less than 20 x 20 pixels) in large-scale images is an important yet open problem.
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
Effective Fusion Factor in FPN for Tiny Object Detection
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Scale Match for Tiny Person Detection
In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising directionfor tiny object detection in a long distance and with mas-sive backgrounds.
Min-Entropy Latent Model for Weakly Supervised Object Detection
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.
A scalable convolutional neural network for task-specified scenarios via knowledge distillation
In this paper, we explore the redundancy in convolutional neural network, which scales with the complexity of vision tasks.
