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Found 32 papers, 12 papers with code
Divide and Conquer: Hybrid Pre-training for Person Search
To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data.
Human Co-Parsing Guided Alignment for Occluded Person Re-identification
Most supervised methods propose to train an extra human parsing model aside from the ReID model with cross-domain human parts annotation, suffering from expensive annotation cost and domain gap; Unsupervised methods integrate a feature clustering-based human parsing process into the ReID model, but lacking supervision signals brings less satisfactory segmentation results.
Ranked #3 on
Person Re-Identification
on Occluded-DukeMTMC
Grouped Adaptive Loss Weighting for Person Search
A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates.
Learning Audio-Visual embedding for Person Verification in the Wild
It has already been observed that audio-visual embedding is more robust than uni-modality embedding for person verification.
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.
Bi-level Alignment for Cross-Domain Crowd Counting
The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking
Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e. g. person detection or appearance-based association of detected persons.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
Adaptive Group Collaborative Artificial Bee Colony Algorithm
As an effective algorithm for solving complex optimization problems, artificial bee colony (ABC) algorithm has shown to be competitive, but the same as other population-based algorithms, it is poor at balancing the abilities of global searching in the whole solution space (named as exploration) and quick searching in local solution space which is defined as exploitation.
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation
In this setting, we embed an additional pair of “latent-latent” to reduce the domain gap between the source and different latent domains, allowing the model to adapt well on multiple target domains simultaneously.
An improved bearing fault detection strategy based on artificial bee colony algorithm
Moreover, XGBoost is used to recognize the faults from the obtained features, and an improved artificial bee colony algorithm(ABC) where the evolution is guided by the importance indices of different search space is proposed to optimize the parameters of XGBoost.
Keypoint Message Passing for Video-based Person Re-Identification
Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras.
Representation Learning
Video-Based Person Re-Identification
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
Multi-Source Unsupervised Domain Adaptation
Object Detection
+1
Adversarial Sample Detection for Speaker Verification by Neural Vocoders
We also show that the neural vocoder adopted in the detection framework is dataset-independent.
Incremental Generative Occlusion Adversarial Suppression Network for Person ReID
The adversarial suppression branch, embedded with two occlusion suppression module, minimizes the generated occlusion’s response and strengthens attentive feature representation on human non-occluded body regions.
Ranked #6 on
Person Re-Identification
on Occluded-DukeMTMC
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models
Embedding of rare and out-of-vocabulary (OOV) words is an important open NLP problem.
PoseTrackReID: Dataset Description
For that reason, we present PoseTrackReID, a large-scale dataset for multi-person pose tracking and video-based person re-ID.
Joint Transceiver Design Based on Dictionary Learning Algorithm for SCMA
With the explosively increasing demands on the network capacity, throughput and number of connected wireless devices, massive connectivity is an urgent problem for the next generation wireless communications.
Regular Expression Guided Entity Mention Mining from Noisy Web Data
Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents.
Person Search via A Mask-Guided Two-Stream CNN Model
In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID).
Occluded Pedestrian Detection Through Guided Attention in CNNs
In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection.
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
CityPersons: A Diverse Dataset for Pedestrian Detection
Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data.
Ranked #14 on
Pedestrian Detection
on Caltech
Semi-supervised Discovery of Informative Tweets During the Emerging Disasters
Given the wide range of possible disasters, using a pre-selected set of disaster-related keywords for the discovery is suboptimal.
How Far are We from Solving Pedestrian Detection?
We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines
However, the DBM is limited in scene recognition due to the fact that natural scene images are usually very large.
Filtered Feature Channels for Pedestrian Detection
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture.
Filtered Channel Features for Pedestrian Detection
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest.
Ranked #29 on
Pedestrian Detection
on Caltech
Informed Haar-like Features Improve Pedestrian Detection
Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model.
