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- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting9,210
An efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: a ProbSparse self-attention mechanism, which achieves O(L log L) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment.
- ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate1,015
A multi-agent referee team called ChatEval is constructed to autonomously discuss and evaluate the quality of generated responses from different models on open-ended questions and traditional natural language generation (NLG) tasks, offering a human-mimicking evaluation process for reliable assessments.
- Adversarial Multiple Source Domain Adaptation638
This paper proposes multisource domain adversarial networks (MDAN) that approach domain adaptation by optimizing task-adaptive generalization bounds and conducts extensive experiments showing superior adaptation performance on both classification and regression problems: sentiment analysis, digit classification, and vehicle counting.
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference451
A text-guided training-free token optimization mechanism dubbed SparseVLM that eliminates the need of extra parameters or fine-tuning costs, and introduces a rank-based strategy to adaptively determine the sparsification ratio for each layer alongside a token recycling method that compresses pruned tokens into more compact representations.
- Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans440
An Self-Trans approach is proposed, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting in COVID-19.
- Q-Diffusion: Quantizing Diffusion Models361
This work proposes a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture of the diffusion models, which compresses the noise estimation network to accelerate the generation process.
- Instance Adaptive Self-training for Unsupervised Domain Adaptation356
To effectively improve the quality of pseudo-labels, a novel pseudo-label generation strategy with an instance adaptive selector is developed and the region-guided regularization to smooth the pseudo- label region and sharpen the non-pseudo-label region is proposed.
- A Review of Single-Source Deep Unsupervised Visual Domain Adaptation349
This article review the latest single-source deep unsupervised DA methods focused on visual tasks and discusses new perspectives for future research, including discrepancy-based methods, adversarial discriminative methods, and self-supervision- based methods.
- PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning293
This paper first collaborate CLIP and GPT to be a unified 3D open-world learner, named as Point-CLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection, demonstrating the generalization ability for unified 3D open-world learning.
- Multi-Source Distilling Domain Adaptation257
This paper proposes a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones.
- Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models241
It is discovered that $\boldsymbol \gamma$ in LayerNorm (LN) acts as a sinful amplifier for the outliers, and the importance of outliers varies greatly where some outliers provided by a few tokens cover a large area but can be clipped sharply without negative impacts.
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- FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras225
A novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count is designed by connecting fully convolutional neural networks (FCN) with long short term memory networks (L STM) in a residual learning fashion and enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count.
- RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete208
RoboBrain is an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities.
- Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation204
An end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA) that not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains.
- RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision186
This work is the first attempt to train multi-view 3D occupancy models only using 2D labels, reducing the dependence on costly 3D occupancy annotations and introduces an Auxiliary Ray method to tackle the issue of sparse viewpoints in autonomous driving scenarios.
- RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation157
This work introduces RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference.
- Understanding Traffic Density from Large-Scale Web Camera Data145
The FCN based method jointly estimates vehicle density and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives.
- Self-Supervised Pretraining Improves Self-Supervised Pretraining142
H Hierarchical PreTraining (HPT) is explored, which decreases convergence time and improves accuracy by initializing the pretraining process with an existing pretrained model, and provides a simple framework for obtaining better pretrained representations with less computational resources.
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- Expanding the prediction capacity in long sequence time-series forecasting126
An efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics, which demonstrate that Informer outperforms existing methods and provides a new solution to the LSTF problem.
- Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation123
This paper proposes the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA), and provides a finite sample bound for both classification and regression problems under Semi-DA.
- PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection117
This work proposes Pi-MAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects, and designs a unique cross-modal reconstruction module to enhance representation learning for both modalities.
- VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model116
VoroNav is introduced, a novel semantic exploration framework that proposes the Reduced Voronoi Graph to extract exploratory paths and planning nodes from a semantic map constructed in real time and surpasses existing benchmarks in both success rate and exploration efficiency.
- Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want114
The Draw-and-Understand framework is presented, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs) and can be easily and effectively applied to various MLLMs, such as SPHINX-X and LLaVA.
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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