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Works84 from public data
- Scale Match for Tiny Person Detection402
A simple yet effective Scale Match approach to align the object scales between the two datasets for favorable tiny-object representation and the significant performance gain of this proposed approach over state-of-the-art detectors is shown.
- Effective Fusion Factor in FPN for Tiny Object Detection343
This paper argues that the top-down connections between adjacent layers in FPN bring two-side influences for tiny object detection, not only positive, and proposes a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN totiny object detection.
- TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization274
This paper introduces the token semantic coupled attention map (TS-CAM) to take full advantage of the self-attention mechanism in visual transformer for long-range dependency extraction and achieves state-of-the-art performance.
- Min-Entropy Latent Model for Weakly Supervised Object Detection232
A min-entropy latent model (MELM) is proposed for weakly supervised object detection, unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.
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- Visual abnormal behavior detection based on trajectory sparse reconstruction analysis100
A novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA), solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small.
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- Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)83
A new approach for visual object tracking based on Sample-Based Adaptive Sparse Representation (AdaSR), which ensures that the tracked object is adaptively and compactly expressed with predefined samples, which is better than those of several representative tracking methods.
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- Multi-Attention Network for Compressed Video Referring Object Segmentation69
A multi-attention network which consists of dual-path dual-att attention module and a query-based cross-modal Transformer module designed to extract effective representation from compressed data in three modalities, i.e., I-frame, Motion Vector and Residual is proposed.
- Rethinking Sampling Strategies for Unsupervised Person Re-Identification67
It is suggested that deteriorated over-fitting is an important factor causing poor performance, and enhancing statistical stability can rectify this problem, and a simple yet effective approach is proposed, termed group sampling, which gathers samples from the same class into groups.
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- Object Localization under Single Coarse Point Supervision40
A coarse point refinement (CPR) approach, which to the best knowledge is the first attempt to alleviate semantic variance from the perspective of algorithm, which ensures training high-performance object localizer under coarse point supervision.
- Combined feature evaluation for adaptive visual object tracking38
A combined feature evaluation approach in filter frameworks for adaptive object tracking by combining color histogram and gradient orientation histogram, which gives a representation of both color and contour.
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- Semantic-aware SAM for Point-Prompted Instance Segmentation34
A Semantic-Aware Instance Segmentation Network (SAPNet) that integrates Multiple Instance Learning with matching capability and SAM with point prompts is devised that strategically selects the most representative mask proposals generated by SAM to supervise segmentation, with a specific focus on object category information.
- SM+: Refined Scale Match for Tiny Person Detection34
Experiments show that SM+ noticeably improves the performance on TinyPerson, and outperforms the state-of-the-art detectors with a significant margin, and a new probabilistic structure inpainting method is proposed for the background processing.
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- The 1st Tiny Object Detection Challenge: Methods and Results33
A brief summary of the 1st Tiny Object Detection (TOD) Challenge is provided including brief introductions to the top three methods.
- End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution25
Experiments on VOC and COCO object detection datasets show that SPE outperforms the state-of-the-art end-to-end methods by 7.0% mAP and 8.1% AP50, and is an order of magnitude faster than the two-stage methods, setting the first solid baseline for end- to-end WSOD with sparse proposals.
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- Wholly-WOOD: Wholly Leveraging Diversified-Quality Labels for Weakly-Supervised Oriented Object Detection22
Wholly-WOOD is developed, a weakly-supervised OOD framework, capable of wholly leveraging various labeling forms (Points, HBoxes, RBoxes, and their combination) in a unified fashion, and achieves performance very close to that of the RBox-trained counterpart on remote sensing and other areas.
- Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes22
A Spatial Self-Distillation based Object Detector (SSD-Det) is heuristically proposed to mine spatial information to refine the inaccurate box in a self-distillation fashion and achieves state-of-the-art performance.
- Consistency-Aware Anchor Pyramid Network for Crowd Localization17
A Consistency-Aware Anchor Pyramid Network (CAAPN) comprising an Adaptive Anchor Generator (AAG) and a Localizer with Augmented Matching (LAM) that achieves favorable performance against state-of-the-art approaches on five challenging datasets.
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- VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence16
The VER-Bench is introduced, a novel framework to evaluate MLLMs' ability to identify fine-grained visual clues and integrate these clues with world knowledge for complex reasoning, highlighting the need to enhance models' capabilities in fine-grained visual evidence extraction, integration, and reasoning for genuine visual understanding and human-like analysis.
- From Coarse to Fine: Hierarchical Structure-aware Video Summarization16
This work generates action proposals to construct the structure of the video and formulate the summarization process as a hierarchical refining process, and trains a hierarchical summarization network with deep Q-learning to achieve the refining process and explore temporal dependency.
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- A bottom-up summarization algorithm for videos in the wild15
A bottom-up approach named clip growing for video summarization, which allows users to customize the quality of the video summaries, and can produce promising results compared to existing algorithms.
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- High performance person re-identification via a boosting ranking ensemble11
This paper proposes a novel framework for person re-identification called Boosting Ranking Ensemble (BRE), which adaptively assembles features and metrics from a ranking perspective and explores an effective rectifier loss in the proposed BRE framework to alleviate the influence of the misclassified samples.
- CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection11
CircleNet implements feature ensemble with the idea of hard negative boosting in an end-to-end manner to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects.
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- Weakly Supervised Video Individual Counting9
A weakly supervised VIC task is introduced, wherein trajectory labels are not provided, and two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow), and results show that the baseline weakly supervised method outperforms supervised methods.
- Spatial Preserved Graph Convolution Networks for Person Re-identification9
A graph convolution network based model for person re-identification is proposed to learn more discriminative feature embeddings, where a graph-structured relationship between person images and person parts are together integrated.
- CPR++: Object Localization via Single Coarse Point Supervision7
This study proposes coarse point refinement (CPR), which to the best knowledge is the first attempt to alleviate semantic variance from an algorithmic perspective in POL, and observes that CPR++ can obtain scale information and further reduce the semantic variance in a global region, thus guaranteeing high-performance object localization.
- A scalable convolutional neural network for task-specified scenarios via knowledge distillation7
A task-specified knowledge distillation algorithm is proposed to derive a simplified model with pre-set computation cost and minimized accuracy loss, which suits the resource constraint front-end systems well and proves the feasibility of the proposed approach as well as the existence of task- specified redundancy.
- Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes6
Evidence-grounded visual reasoning (EGVOR) is proposed, departing from implicit inference, EGVOR reformulates reasoning as the explicit generation of Evidence Atoms–structured triplets that enforce strict spatial-semantic alignment.
- ClickTrack: Towards real-time interactive single object tracking6
A new paradigm for single object tracking algorithms, ClickTrack, a new paradigm using clicking interaction for real-time scenarios, and the Guided Click Refiner (GCR), which accepts point and optional textual information as inputs, transforming the point into the bounding box expected by the operator.
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- Feature evaluation by particle filter for adaptive object tracking6
An online combined feature evaluation method for visual object tracking that adapts to changing appearances of both tracked object and background and extends the traditional filter framework from modeling motion states to modeling feature evaluation.
- Boosting Segment Anything Model Towards Open-Vocabulary Learning5
This paper introduces a novel SideFormer module designed to acquire SAM features adept at perceiving objects and inject comprehensive semantic information for recognition, and presents Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
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- “Signal, image and video processing” special issue: Semantic representations for social behavior analysis in video surveillance systems4
A number of feature representation approaches have been reported to address the coherence between the extracted features and the semantic interpretations in the gap between the information directly extracted from videos and semantic interpretations by human beings.
- A Belief Based Correlated Topic Model for Semantic Region Analysis in Far-Field Video Surveillance Systems4
Experiments show that the proposed belief based correlated topic model (BCTM) outperforms the state-of-the-art methods on qualitative results of learning semantic regions.
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- Visual Object Tracking via One-Class SVM3
A new visual object tracking approach via one-class SVM (OC-SVM), inspired by the fact that OCSVM's support vectors can form a hyper-sphere, whose center can be regarded as a robust object estimation from samples, is proposed.
- P2Object: Single Point Supervised Object Detection and Instance Segmentation2
This paper introduces Point-to-Box Network (P2BNet), which constructs balanced instance-level proposal bags by generating proposals in an anchor-like way and refining the proposals in a coarse-to-fine paradigm, and largely surpasses the previous methods in terms of the mean average precision.
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- NRPerson: A Non-Registered Multi-Modal Benchmark for Tiny Person Detection and Localization–
A novel non-registered multi-modal benchmark named NRPerson is introduced, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios.
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- Robust Visual Object Tracking via Sparse Representation and Reconstruction–
This paper proposes a new approach for robust visual object tracking via sparse representation and reconstruction, where two main contributions are devoted in terms of object representation and location respectively.
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- Real-time pedestrian learning-tracking with information fusion–
The results of the experiments show good robustness and performance when facing extreme cases such as the object rotation, long-period occlusion, and high similarity between object and background.
- Visual object tracking via sparse reconstruction–
With the coefficient vector of the sparse representation, the tracked object is reconstructed in an instantaneous sample set, which improves the tracking adaptation to background variation, object shape change, and partial occlusion.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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