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- LISA: Reasoning Segmentation via Large Language Model1,078
This work proposes a new segmentation task - reasoning segmentation and presents LISA: large Language Instructed Segmentation Assistant, which inherits the language generation capabilities of multimodal Large Language Models (LLMs) while also possessing the ability to produce segmentation masks.
- Mini-Gemini: Mining the Potential of Multi-Modality Vision Language Models415
This work introduces Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs), and proposes to utilize an additional visual encoder for high-resolution refinement without increasing the visual token count to enhance visual tokens.
- Unifying Voxel-based Representation with Transformer for 3D Object Detection401
The proposed method achieves leading performance in the nuScenes test set for both object detection and the following object tracking task, and surpasses previous work in single- or multi-modality entries.
- Focal Sparse Convolutional Networks for 3D Object Detection326
This paper introduces two new modules to enhance the capability of Sparse CNNs, both are based on making feature sparsity learnable with position-wise importance prediction and shows that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection.
- Attention-Guided Unified Network for Panoptic Segmentation293
The underlying relationship between FG objects and BG contents is revealed, in particular, FG objects provide complementary cues to assist BG understanding, and the Attention-guided Unified Network (AUNet) is named, a unified framework with two branches for FG and BG segmentation simultaneously.
- Fully Convolutional Networks for Panoptic Segmentation202
This approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline and outperforms previous box-based and -free models with high efficiency on COCO, Cityscapes, and Mapillary Vistas datasets with single scale input.
- Learning Dynamic Routing for Semantic Segmentation190
A conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing, which generates data-dependent routes, adapting to the scale distribution of each image, and compares with several static architectures, which can be modeled as special cases in the routing space.
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- Voxel Field Fusion for 3D Object Detection121
This work presents a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion, and develops mixed augmentor to align feature-variant transformations, which bridges the modality gap in data augmentation.
- Stitcher: Feedback-driven Data Provider for Object Detection76
Stitcher is presented, a feedback-driven data provider, which aims to train object detectors in a balanced way, and steadily improves performance by a large margin in all settings, especially for small objects, with nearly no additional computation in both training and testing stages.
- Multi-Scale Aligned Distillation for Low-Resolution Detection74
The idea of spatially aligning feature maps between models of varying input resolutions by shifting feature pyramid position is explored and crossed feature-level fusion is proposed to dynamically fuse teacher’s multi-resolution features to guide the student better.
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- Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision65
A conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN, which encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
- Development of an Immunochromatographic Strip Test for Rapid Detection of Melamine in Raw Milk, Milk Products and Animal Feed65
A simple, rapid and sensitive immunogold chromatographic strip test based on a monoclonal antibody for the detection of melamine (MEL) residues in raw milk, milk products and animal feed demonstrates that the developed method is suitable for the onsite determination of MEL residues in a large number of samples.
- State-Aware Re-Identification Feature for Multi-Target Multi-Camera Tracking42
In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered and the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem.
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- Vibrio harveyi co-infected with Cryptocaryon irritans to orange-spotted groupers Epinephelus coioides26
Findings provide the evidence of coinfections with C. irritans and V. harveyi in orange-spotted grouper.
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- Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines21
This work proposes an attention-aware learning method that integrates the parameter prediction network into ISP tuning and utilizes the multi-attention mechanism to generate the attentive mapping be-tween the input RAW image and the parameter space.
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- Variations of the Functional Brain Network Efficiency in a Young Clinical Sample within the Autism Spectrum: A fNIRS Investigation20
Results suggest individual differences of brain development in young children within the Autism spectrum, providing new insights into the psychopathology of ASD.
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- An efficient and privacy-preserving truth discovery scheme in crowdsensing applications18
This work proposes an efficient and privacy-preserving truth discovery framework (EPTD-I) by adopting a novel data perturbation mechanism and improves the design with a user non-interactive scheme named EPTD-II to shift all encrypted truth discovery operations to cloud platforms.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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