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Works87 from public data
- LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning290
LL3DA is presented, a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions that enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts.
- $\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search155
A simple-but-efficient regularization method, termed as Beta-Decay, is proposed to regularize the DARTS-based NAS searching process and can impose constraints to keep the value and variance of activated architecture parameters from too large.
- DualMamba: A Lightweight Spectral–Spatial Mamba-Convolution Network for Hyperspectral Image Classification94
A novel lightweight parallel design called a lightweight dual-stream Mamba-convolution network (DualMamba) for HSI classification achieves significant classification accuracy on three public HSI datasets and a superior reduction in model parameters and floating-point operations (FLOPs).
- Landmark recognition with compact BoW histogram and ensemble ELM87
A landmark recognition framework is proposed by employing a novel discriminative feature selection method and the improved extreme learning machine (ELM) algorithm to generate a set of preliminary codewords for landmark images.
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- A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction58
A large-scale outdoor multi-modal dataset, OMMO dataset, containing complex objects and scenes with calibrated images, point clouds and prompt annotations is proposed, establishing a new benchmark for several outdoor NeRF-based tasks, such as novel view synthesis, diverse 3D representation, and multi-modal NeRF.
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- Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification52
Extensive experiments demonstrate that HelixFormer can effectively enhance the cross-image object semantic relation matching for recognizing fine-grained objects, achieving much better performance over most state-of-the-art methods under 1-shot and 5-shot scenarios.
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- Boost Vision Transformer with GPU-Friendly Sparsity and Quantization50
A compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization of vision transformer models that is flexible to support supervised and unsupervised learning styles.
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- Performance-Aware Approximation of Global Channel Pruning for Multitask CNNs44
Experiments on several multitask datasets show that the proposed PAGCP can reduce the FLOPs and parameters by over 60% with minor performance drop, and achieves 1.3x acceleration on both cloud and mobile platforms.
- Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification41
A novel diffusion-based feature learning framework that explores multi-timestep multi-stage diffusion features for HSI classification for the first time, called MTMSD, which outperforms state-of-the-art methods for HSI classification, especially on the challenging Houston 2018 dataset.
- Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification41
This work proposes an object-aware long-short-range spatial alignment approach, which is composed of a foreground object feature enhancement (FOE) module, a long-range semantic correspondence (LSC) module and a short-rangeatial manipulation (SSM) module that is developed to weaken background disturbance and encourage higher foreground object response.
- What Makes for Effective Few-shot Point Cloud Classification?34
A novel plug-and-play component called Cross-Instance Adaptation (CIA) module is proposed, to address the high intra-class variances and subtle inter-class differences issues, which can be easily inserted into current baselines with significant performance improvement.
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- Point Cloud Instance Segmentation With Semi-Supervised Bounding-Box Mining28
This paper introduces the first semi-supervised point cloud instance segmentation framework (SPIB) using both labeled and unlabelled bounding boxes as supervision and introduces a novel occupancy ratio guided refinement module to refine the instance masks.
- Densely Semantic Enhancement for Domain Adaptive Region-Free Detectors28
The proposed adversarial module, namely, densely semantic enhancement module (DSEM), is proposed to strengthen the cross-domain matching of instance-level features for region-free detectors and outperforms existing domain adaptive region-based detectors under various domain shift settings.
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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 2026-10-12. 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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