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- Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking382
The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data.
- Multimodal Industrial Anomaly Detection via Hybrid Fusion297
This paper proposes Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme that outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec- 3D AD dataset.
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- Rethinking Reverse Distillation for Multi-Modal Anomaly Detection68
A novel Multi-Modal Reverse Distillation paradigm that consists of a frozen multi-modal teacher encoder to generate distillation targets and a learnable student decoder targeting to restore multi-modal representations from the teacher is presented.
- FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on49
A novel garment perception enhancement technique, termed FitDiT, designed for high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more parameters and attention to high-resolution features, and introduces frequency-domain learning by customizing a frequency distance loss to enhance high-frequency garment details.
- FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow47
FOLT adopts a modern detector and light-weight optical flow extractor to extract object detection features and motion features at a minimum cost and outperform existing state-of-the-art methods in UAV-MOT tasks.
- SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking42
A novel siamese tracking algorithm called SiamRCR is proposed, addressing this problem with a simple, light and effective solution, and builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample.
- M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising38
A novel noise-resistant M3DM-NR framework to leveraging strong multi-modal discriminative capabilities of CLIP, which outperforms state-of-the-art methods in 3D-RGB multi-modal noisy anomaly detection.
- Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region32
Compositional Neural Painter is proposed, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions.
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- YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection23
YOLO-Master, a novel YOLO-like framework that introduces instance-conditional adaptive computation for RTOD, is proposed through a Efficient Sparse Mixture-of-Experts (ES-MoE) block that dynamically allocates computational resources to each input according to its scene complexity.
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- MM-Tracker: Motion Mamba for UAV-platform Multiple Object Tracking19
This work proposes the Motion Mamba Module, which explores both local and global motion features through cross-correlation and bi-directional Mamba Modules for better motion modeling, and designs motion margin loss to effectively improve the detection accuracy of motion blurred objects.
- STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation19
This work presents a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head and proposes a novel bi-directional spatio-temporal contrastive learning strategy for tracking embedding across frames.
- Dense Scene Multiple Object Tracking with Box-Plane Matching19
The Box-Plane Matching method is proposed to improve the MOT performacne in dense scenes and achieve data association according to the motion similarity and appearance similarity between tracklets and detections.
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- Toward High Quality Facial Representation Learning15
A self-supervised pre-training framework, called Mask Contrastive Face (MCF), with mask image modeling and a contrastive strategy specially adjusted for face domain tasks is proposed, which outperforms the state-of-the-art methods on multiple downstream tasks.
- Mamba-YOLO-World: Marrying YOLO-World with Mamba for Open-Vocabulary Detection14
Mamba-YOLO-World is presented, a novel YOLO-based OVD model employing the proposed MambaFusion Path Aggregation Network (MambaFusion-PAN) as its neck architecture and surpasses existing state-of-the-art OVD methods with fewer parameters and FLOPs.
- DMAD: Dual Memory Bank for Real-World Anomaly Detection14
The results show that DMAD surpasses current state-of-the-art methods, highlighting DMAD's capability in handling the complexities of real-world anomaly detection scenarios.
- PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models12
PointSeg is presented, a novel training-free paradigm that leverages off-the-shelf vision foundation models to address 3D scene perception tasks and demonstrates impressive segmentation performance across various datasets, all without training.
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- Fetch and Forge: Efficient Dataset Condensation for Object Detection11
This work proposes DCOD, the first dataset condensation framework for object detection, which operates in two stages: Fetch and Forge, initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion.
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- DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation6
This work proposes a novel framework, DynamicControl, which supports dynamic combinations of diverse control signals, allowing adaptive selection of different numbers and types of conditions, and demonstrates its superiority over existing methods in terms of controllability, generation quality and composability under various conditional controls.
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 2026-10-14. 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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