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Works172 from public data
- Recent advances and trends in visual tracking: A review651
The goal of this paper is to review the state-of-the-art progress on visual tracking methods, classify them into different categories, as well as identify future trends.
- Deep Industrial Image Anomaly Detection: A Survey514
This paper provides a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
- 428
- Deep 3D human pose estimation: A review402
A thorough review of existing deep learning based works for 3D pose estimation is provided, the advantages and disadvantages of these methods are summarized, and the commonly-used benchmark datasets are explored for comparison and analysis.
- Video Understanding With Large Language Models: A Survey332
The emergent capabilities of Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity (abstract, temporal, and spatiotemporal) reasoning combined with common-sense knowledge, suggesting a promising path for future video understanding.
- Deep Spectral Clustering Using Dual Autoencoder Network313
A joint learning framework for discriminative embedding and spectral clustering is proposed, which can significantly outperform state-of-the-art clustering approaches and be more robust to noise.
- 265
- End-to-End Dense Video Captioning with Parallel Decoding260
Without bells and whistles, extensive experiments show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art two-stage methods when its localization accuracy is on par with them.
- FREE: Feature Refinement for Generalized Zero-Shot Learning229
A feature refinement module that in-corporates semantic→visual mapping into a unified generative model to refine the visual features of seen and unseen class samples, and a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations.
- Prompting for Multi-Modal Tracking215
A novel multi-modal prompt tracker (ProTrack), which can transfer the multi- modal inputs to a single modality by the prompt paradigm, and can achieve high-performance multi- Modal tracking by only altering the inputs, even without any extra training on multi-Modal data.
- Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models178
A highly transferable Set-level Guidance Attack (SGA) that thoroughly leverages modality interactions and incorporates alignment-preserving augmentation with cross-modal guidance is proposed that could generate adversarial examples that can strongly transfer across different VLP models on multiple downstream vision-language tasks.
- 159
- IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing141
A uniform IM benchmark is proposed, for the first time, to assess how well IAD algorithms perform, which includes various levels of supervision, learning paradigms, memory usage and inference speed, and efficiency.
- Saliency-Associated Object Tracking141
This paper proposes a fine-grained saliency mining module to capture the local saliencies of the target that are discriminative for tracking and designs a saliency-association modeling module to associate the captured saliencies together to learn effective correlation representations between the exemplar and the search image for state estimation.
- Class-Aware Contrastive Semi-Supervised Learning140
This work proposes a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting and successfully emphasizes clean label learning and simultaneously reduce noisy label learning.
- Does Thermal Really Always Matter for RGB-T Salient Object Detection?136
A global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities, and proposes a network named TNet to solve the RGB-T SOD task.
- 130
- DepthTrack: Unveiling the Power of RGBD Tracking130
A new RGBD tracking dataset - Depth-Track - is introduced that has twice as many sequences and scene types than in the largest existing dataset, and three times more objects, and the average length of the sequences, number of deformable objects and the number of annotated tracking attributes have been increased.
- 120
- Deep learning for video object segmentation: a review109
A systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches and summarising the datasets for training and testing a video object segmentation algorithm.
- A parallel down-up fusion network for salient object detection in optical remote sensing images108
A novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
- 106
- Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification106
This work proposes a novel approach to viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation and demonstrates that it outperforms the state-of-the-art supervised Re-ID methods.
- EasyNet: An Easy Network for 3D Industrial Anomaly Detection103
This work proposes an easy and deployment-friendly network (called EasyNet) without using pretrained models and memory banks, which is faster than existing methods, and adopts a multi-modality anomaly segmentation network to achieve a precise anomaly map.
- Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection99
This work introduces CNNs-assisted Transformer architecture and proposes a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement (PICR-Net), which achieves competitive results in both quantitative and qualitative comparisons.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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