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Found 32 papers, 15 papers with code
Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary Segmentation
Then, based on the theory, we design a DPD algorithm which is composed by a training paradigm and proxy domain generator to enhance the domain generalization of the confidence-threshold learner.
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
Considering the absence of a dataset for this task, a large-scale Dataset (NWPU-MOC) is collected, consisting of 3, 416 scenes with a resolution of 1024 $\times$ 1024 pixels, and well-annotated using 14 fine-grained object categories.
SamLP: A Customized Segment Anything Model for License Plate Detection
Meanwhile, the proposed SamLP has great few-shot and zero-shot learning ability, which shows the potential of transferring vision foundation model.
Test-time Adaptive Vision-and-Language Navigation
Then, these components are adaptively accumulated to pinpoint a concordant direction for fast model adaptation.
Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
In this manner, our framework is able to learn the unified representations of uni-modal or multi-modal skeleton input, which is flexible to different kinds of modality input for robust action understanding in practical cases.
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation
Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting their applications in the scenarios that require safe and controllable model decisions.
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Theoretically, we show the convergence guarantee of the dual regulators.
Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing
In this paper, to balance the feature learning processes of different modalities, a dynamic gradient modulation (DGM) mechanism is explored, where a novel and effective metric function is designed to measure the imbalanced feature learning between audio and visual modalities.
Imbalanced Aircraft Data Anomaly Detection
Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information with temporal correlation; 2) the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail.
Cascade Evidential Learning for Open-World Weakly-Supervised Temporal Action Localization
Targeting at recognizing and localizing action instances with only video-level labels during training, Weakly-supervised Temporal Action Localization (WTAL) has achieved significant progress in recent years.
Open Set Learning
Weakly-supervised Temporal Action Localization
+1
Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception
We argue that, for an event residing in one modality, the modality itself should provide ample presence evidence of this event, while the other complementary modality is encouraged to afford the absence evidence as a reference signal.
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #6 on
Object Counting
on FSC147
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
The mainstream crowd counting methods regress density map and integrate it to obtain counting results.
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting
RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds.
Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher
The core reason of intrinsic scale shift being one of the most essential issues in crowd localization is that it is ubiquitous in crowd scenes and makes scale distribution chaotic.
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification
Most existing state-of-the-art video classification methods assume that the training data obey a uniform distribution.
Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding
Then, a commonsense-aware interaction module is designed to obtain bridged visual and text features by utilizing the learned commonsense concepts.
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
We target at the task of weakly-supervised action localization (WSAL), where only video-level action labels are available during model training.
DR.VIC: Decomposition and Reasoning for Video Individual Counting
Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame.
Weakly-Supervised Video Object Grounding via Causal Intervention
With this in mind, we design a unified causal framework to learn the deconfounded object-relevant association for more accurate and robust video object grounding.
Audio-visual Representation Learning for Anomaly Events Detection in Crowds
The second is an audio CNN for encoding Log Mel-Spectrogram of audio signals.
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map.
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain).
Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer
To this end, we propose a Dilated Convolutional Swin Transformer (DCST) for congested crowd scenes.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Health Status Prediction with Local-Global Heterogeneous Behavior Graph
However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging.
Fast Video Moment Retrieval
To tackle this issue, we replace the cross-modal interaction module with a cross-modal common space, in which moment-query alignment is learned and efficient moment search can be performed.
Active Universal Domain Adaptation
This paper proposes a new paradigm for unsupervised domain adaptation, termed as Active Universal Domain Adaptation (AUDA), which removes all label set assumptions and aims for not only recognizing target samples from source classes but also inferring those from target-private classes by using active learning to annotate a small budget of target data.
Learning Independent Instance Maps for Crowd Localization
Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating.
I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs
To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch.
Ranked #5 on
Zero-Shot Action Recognition
on Olympics
