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Works221 from public data
- CenterNet: Keypoint Triplets for Object Detection3,587
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- CenterNet: Object Detection with Keypoint Triplets3,563
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
- Accurate medium-range global weather forecasting with 3D neural networks2,041
It is shown that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting.
- 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering1,670
This work proposes 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame, and achieves real-time rendering under high resolutions, 82 FPS at an 800x800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state- of-the-art methods.
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- Adversarial Examples for Semantic Segmentation and Object Detection1,057
This paper proposes a novel algorithm named Dense Adversary Generation (DAG), which applies to the state-of-the-art networks for segmentation and detection, and finds that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks.
- Genetic CNN942
The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string to efficiently explore this large search space.
- Genetic CNN942
The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string to efficiently explore this large search space.
- Genetic CNN942
The core idea is to propose an encoding method to represent each network structure in a fixed-length binary string to efficiently explore this large search space.
- Conformer: Local Features Coupling Global Representations for Visual Recognition827
Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet and on MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network.
- Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation751
This paper presents an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure, and solves two issues, namely, heavier computational overheads and weaker search stability, which are solved using search space approximation and regularization.
- Fast Dynamic Radiance Fields with Time-Aware Neural Voxels492
This framework significantly accelerates the optimization of dynamic radiance fields while maintaining high rendering quality and completes training with only 8 minutes and 8-MB storage cost while showing similar or even better rendering performance than previous dynamic NeRF methods.
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- SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images362
A large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated, and proposes an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties.
- GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models339
This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation via a fast 3D object gener-ation framework, named as GaussianDreamer, where the 3D diffusion model provides priors for initial-ization and the 2D diffusion model enriches the geometry and appearance.
- Visformer: The Vision-friendly Transformer328
A new architecture named Visformer is proposed, which is abbreviated from the ‘Vision-friendly Transformer’, which outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy, and the advantage becomes more significant when the model complexity is lower or the training set is smaller.
- A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans317
This paper forms a fixed-point model which uses a predicted segmentation mask to shrink the input region and outperform the state-of-the-art by more than \(4\%\), measured by the average Dice-Sorensen Coefficient (DSC).
- Unsupervised Person Re-Identification via Softened Similarity Learning307
The iterative training mechanism is followed but clustering is discarded, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task.
- Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast301
Pangu-Weather not only ends the debate on whether AI-based methods can surpass conventional NWP methods, but also reveals novel directions for improving deep learning weather forecast systems.
- 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.
- DisturbLabel: Regularizing CNN on the Loss Layer266
An extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration, which prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets.
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- QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models227
A quantization-aware low-rank adaptation (QA-LoRA) algorithm that equips the original LoRA with two-fold abilities: during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; and after fine- Tuning,the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy.
Publication data from OpenAlex; 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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