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- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks1,364
This work introduces Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM), which enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models.
- 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.
- VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking524
This paper shows that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking, and proposes VoxelNext, an elegant and efficient framework with no need for sparse-to-dense conversion or NMS post-processing.
- VisionZip: Longer is Better but Not Necessary in Vision Language Models362
The proposed VisionZip is a simple yet effective method that selects a set of informative tokens for input to the language model, reducing visual token redundancy and improving efficiency while maintaining model performance.
- 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.
- LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models297
LongLoRA is presented, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost, and is compatible with most existing techniques, like Flash-Attention2.
- Spherical Transformer for LiDAR-Based 3D Recognition270
This work study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones, and design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows.
- NVILA: Efficient Frontier Visual Language Models260
NVILA, a family of open VLMs designed to optimize both efficiency and accuracy, is introduced by improving its model architecture by first scaling up the spatial and temporal resolutions, and then compressing visual tokens, which enables NVILA to efficiently process high-resolution images and long videos.
- 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.
- LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs174
This work proposes the spatial-wise partition convolution and its large-kernel module, which avoids the optimization and efficiency issues of naive 3D large kernels and shows that large kernels are feasible and essential for 3D visual tasks.
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- FocalFormer3D : Focusing on Hard Instance for 3D Object Detection133
This work proposes Hard Instance Probing (HIP), a general pipeline that identifies FN in a multi-stage manner and guides the models to focus on excavating difficult instances, and instantiates this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
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- Voxel Field Fusion for 3D Object Detection122
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.
- OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation103
The creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost, is proposed.
- DetNAS: Neural Architecture Search on Object Detection.94
This paper proposes DetNAS to automatically search neural architectures for the backbones of object detectors, formulated into a supernet and the search method relies on evolution algorithm (EA).
- SEED-Story: Multimodal Long Story Generation with Large Language Model86
This work introduces SEED-Story, a novel approach that extends Multi-modal Large Language Models (MLLMs) to generate coherent, extended narratives composed of both interleaved text and images, and introduces a multimodal attention sink mechanism to overcome the train-short test-long challenge.
- IST-Net: Prior-free Category-level Pose Estimation with Implicit Space Transformation81
A simple prior-free implicit space transformation network, namely IST-Net, is introduced to transform camera-space features to world-space counterparts and build correspondences between them in an implicit manner without relying on 3D priors, with state-of-the-art performance based on prior-free design.
- 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.
- Mask-Attention-Free Transformer for 3D Instance Segmentation67
This work abandons the mask attention design and resorts to an auxiliary center regression task instead, which effectively overcome the low-recall issue and perform cross-attention by imposing positional prior.
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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.
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- Identification of hepatocellular carcinoma related genes with k -th shortest paths in a protein–protein interaction network53
A computational method is developed to identify hepatocellular carcinoma related genes based on k-th shortest paths in the protein-protein interaction (PPI) network and it is found that 33 genes whose p-values were less than 0.05 have been reported to be involved in HCC tumorigenesis and development.
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 2026-10-16. 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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