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- Richer Convolutional Features for Edge Detection1,240
RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets.
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- Multiple Instance Detection Network with Online Instance Classifier Refinement496
This work formulate weakly supervised object detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes and instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online.
- PCL: Proposal Cluster Learning for Weakly Supervised Object Detection408
This paper first shows that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then shows that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method.
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- Relaxed Multiple-Instance SVM with Application to Object Discovery90
This paper proposes a novel method to solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM), which treats the positiveness of instance as a continuous variable, use Noisy-OR model to enforce the MIL constraints, and optimize them jointly in a unified framework.
- Deep patch learning for weakly supervised object classification and discovery76
The results show that the proposed weakly supervised patch features via weak supervisions method can obtain state-of-the-art performance on object classification, and very competitive results on object discovery, with faster testing speed than competitors.
- Anisotropic-Scale Junction Detection and Matching for Indoor Images41
A novel approach for junction detection and characterization is presented, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model.
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- SEED: A Simple and Effective 3D DETR in Point Clouds36
A simple and effective 3D DETR method (SEED) for detecting 3D objects from point clouds, which involves a dual query selection (DQS) module and a deformable grid attention (DGA) module, which achieves state-of-the-art detection performance on both the large-scale Waymo and nuScenes datasets.
- Research and Perspective on Shape Matching27
The basic concept of shape matching is introduced, the most recent approaches about shape representation and matching for both contour-based and skeleton-based methods are reviewed, and some widely used benchmarks for shape matching are introduced.
- An open dataset for the evolution of oracle bone characters: EVOBC23
An extensive dataset, namely EVOBC, consisting of 229,170 images representing 13,714 distinct character categories is constructed, facilitating the decipherment of oracle bone script by examining the evolution of glyph forms.
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- Robust Subspace Discovery via Relaxed Rank Minimization19
An efficient and effective algorithm based on the alternating direction method of multipliers is given and a new formulation to simultaneously infer the label information and learn the model using low-rank optimization is proposed.
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- Object Recognition Using Junctions15
An object detection/recognition algorithm based on a new set of shape-driven features and morphological operators that is robust to a certain degree of scale change and has advantages of recognizing object parts and dealing with occlusions.
- Texture Characterization Using Shape Co-Occurrence Patterns14
This paper presents a flexible shape-based texture representation using shape co-occurrence patterns of explicit shapes, which provides a flexible way to consider shape relationships and to compute high-order statistics on the tree.
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- UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs5
A unified autonomous driving model, UniLION, which efficiently handles large-scale LiDAR point clouds, high-resolution multi-view images, and even temporal sequences based on the linear group RNN operator (i.e., performs linear RNN for grouped features).
- One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery5
This paper proposes an algorithm using the robust PCA (RPCA) in an iterative way to perform simultaneous common object discovery and model learning under a one-class multiple instance learning setting, which achieves comparable results with standard two-class MIL learning algorithms.
- K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning4
K-ESConv is proposed, a novel prompt learning based knowledge injection method for emotional support dialogue system, transferring forum knowledge to response generation, which outperforms existing baselines on both automatic evaluation and human evaluation.
- Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision4
This special issue aims at presenting recent advancements in both algorithmic and application-driven research related to multiinstance learning, and accepted 5 papers after a strict review process.
- Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation4
A new representation named directional edges is developed on which each edge pixel is assigned with a direction toward object center, through learning a direction prediction model with convolutional neural networks in a holistic manner.
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- Training-Free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing–
Coefficients Learning (CL), a novel training-free framework for sparse reconstruction in Compressed Sensing, retains the interpretability and generality of traditional iterative methods by adopting their residual-based solving process, while enhancing efficiency and accuracy by replacing closed-form solutions with automatic differentiation and embedding prior knowledge into the model losses.
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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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