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- PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud3,019
Extensive experiments on the 3D detection benchmark of KITTI dataset show that the proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input.
- StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks2,975
This paper proposes Stacked Generative Adversarial Networks (StackGAN) to generate 256 photo-realistic images conditioned on text descriptions and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection2,374
The proposed PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins and deeply integrates both 3D voxel Convolutional Neural Network and PointNet-based set abstraction to learn more discriminative point cloud features.
- CLIP-Adapter: Better Vision-Language Models with Feature Adapters1,954
This paper proposes CLIP-Adapter, a novel approach to vision-language pretraining that adopts an additional bottleneck layer to learn new features and performs residual-style feature blending with the original pretrained features and is able to outperform context optimization while maintaining a simple design.
- InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions1,217
This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs, and reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large- scale parameters from massive datalike ViTs.
- StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks1,198
Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.
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- LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention1,068
A zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge on traditional vision and language tasks, demonstrating the superior generalization capacity of the approach.
- From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network1,046
This paper extends the preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network, which outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D objects detection dataset by utilizing only the LiDAR point cloud data.
- Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification997
This work presents a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks with CNNs and proposes a Domain Guided Dropout algorithm to improve the feature learning procedure.
- 995
- Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis978
The Human Preference Score v2 (HPS v2) is obtained, a scoring model that can more accurately predict human preferences on generated images and is responsive to algorithmic improvements of text-to-image generative models, making it a preferable evaluation metric for these models.
- Balanced Meta-Softmax for Long-Tailed Visual Recognition868
Balanced Softmax is presented, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing, and it is demonstrated that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
- Efficient Attention: Attention with Linear Complexities822
A novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs is proposed, which allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies.
- LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model774
This work augments LLaMA-Adapter by unlocking more learnable parameters and proposes an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset.
- Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation756
A new framework for the outdoor LiDAR segmentation is proposed, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties.
- Group-Wise Correlation Stereo Network747
Group-wise correlation provides efficient representations for measuring feature similarities and will not lose too much information like full correlation, and preserves better performance when reducing parameters compared with previous methods.
- 725
- Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID691
This work proposes a novel self-paced contrastive learning framework with hybrid memory that outperforms state-of-the-arts on multiple domain adaptation tasks of object re-ID and even boosts the performance on the source domain without any extra annotations.
- UniFormer: Unifying Convolution and Self-Attention for Visual Recognition646
This work proposes a novel Unified transFormer (UniFormer), which can seamlessly integrate the merits of convolution and self-attention in a concise transformer format, and builds an efficient UniFormer with a concise hourglass design of token shrinking and recovering, which achieves high throughput.
- PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection631
The experiments demonstrate that the proposed PV-RCNN++ framework achieves state-of-the-art 3D detection performance on the large-scale and highly-competitive Waymo Open Dataset with 10 FPS inference speed on the detection range of 150 m × 150 m.
- Person Search with Natural Language Description593
An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search and a large-scale person description dataset with detailed natural language annotations and person samples from various sources is collected.
- T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos548
A deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos is proposed, called T-CNN.
- Learning Feature Pyramids for Human Pose Estimation536
This work designs a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs and provides theoretic derivation to extend the current weight initialization scheme to multi-branch network structures.
- 3D Object Detection for Autonomous Driving: A Comprehensive Survey508
A comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR- based, camera-based, and multi-modal detection approaches, and an in-depth analysis of the potentials and challenges in each category of methods are provided.
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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