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- RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation244
This paper introduces RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes, the largest multi-embodiment teleoperation dataset collected on a unified platform.
- Perceiving and Modeling Density for Image Dehazing156
A novel Separable Hybrid Attention (SHA) module is proposed to perceive haze density by capturing positional-sensitive features in the orthogonal directions to achieve this goal and a density encoding matrix is proposed to model the uneven distribution of the haze explicitly.
- NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer143
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of the NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
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- Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks89
This paper proposes a Sparse Sampling Transformer based on Uncertainty-Driven Ranking, dubbed UDR-S2Former, which can adaptively sample relevant image degradation information to model underlying degradation relationships and clearly outperforms state-of-the-art methods for all benchmarks.
- MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing88
A multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation, achieves the SOTA performance on three desnowing benchmark datasets while costing the low parameters and computational complexity.
- NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results85
This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.
- Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement68
Thorough extensive experiments and comprehensive visual comparison, it is shown that FA$^{+}$Net outperforms previous approaches by obtaining state-of-the-art performance on multiple datasets while significantly reducing both parameter count and computational complexity.
- Underwater Light Field Retention : Neural Rendering for Underwater Imaging68
UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models and is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images.
- Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation61
The Lift3D framework is proposed, which progressively enhances 2D foundation models with implicit and explicit 3D robotic representations to construct a robust 3D manipulation policy and consistently outperforms previous state-of-the-art methods across several simulation benchmarks and real-world scenarios.
- JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration55
JarvisIR, a VLM-powered agent that leverages the VLM as a controller to manage multiple expert restoration models to enhance system robustness, reduce hallucinations, and improve generalizability in real-world adverse weather, is proposed.
- SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing55
A powerful architecture dubbed as SnowFormer performs Scale-aware Feature Aggregation in the encoder to capture rich snow information of various degradations and uses a novel Context Interaction Trans-former Block in the decoder in order to tackle with large-scale degradation.
- Roles of Identified Long Noncoding RNA in Diabetic Nephropathy52
Recent studies reported that the progression of DN is closely related to the expression of long noncoding RNA (lncRNA), which is important for the early diagnosis and targeted intervention of DN.
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- SegDINO: An Efficient Design for Medical and Natural Image Segmentation with DINO-V340
SegDINO is an efficient segmentation framework that couples a frozen DINOv3 backbone with a lightweight decoder, and utilizes a lightweight MLP head to directly predict segmentation masks, which minimizes trainable parameters while preserving the representational power of foundation features.
- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
- Jieduquyuziyin Prescription Suppresses Inflammatory Activity of MRL/lpr Mice and Their Bone Marrow-Derived Macrophages via Inhibiting Expression of IRAK1-NF-κB Signaling Pathway32
It is demonstrated that JP could inhibit the inflammatory activity of MRL/lpr mice and their BMDMs by suppressing the activation of IRAK1-NF-κB signaling and was supposed to be a good choice for the treatment of SLE.
- An Empirical Study of GPT-4o Image Generation Capabilities31
An empirical study of GPT-4o's image generation capabilities is conducted, benchmarking it against leading open-source and commercial models and identifying promising directions for future unified generative models, emphasizing the role of architectural design and data scaling.
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- FastDriveVLA: Efficient End-to-End Driving via Plug-and-Play Reconstruction-based Token Pruning29
FastDriveVLA is proposed, a novel reconstruction-based vision token pruning framework designed specifically for autonomous driving that includes a plug-and-play visual token pruner called ReconPruner, which prioritizes foreground information through MAE-style pixel reconstruction.
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- A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation21
Self-Corrected (SC)-MLLM agent significantly improve manipulation accuracy compared to previous state-of-the-art robotic MLLM (ManipLLM), increasing from 57% to 79% on seen object categories and from 47% to 69% on unseen novel categories.
- Semi-supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization20
This work constructs a real-world dataset with 85 snowy videos, and presents a Semi-supervised Video Desnowing Network (SemiVDN) equipped by a novel Distribution-driven Contrastive Regularization that mitigates the distribution gap between the synthetic and real data, and consequently maintains the desired snow-invariant background details.
- SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization19
- Uncertainty-Driven Dynamic Degradation Perceiving and Background Modeling for Efficient Single Image Desnowing19
This paper proposes Dynamic Perceiving for Degraded Regions and Axial-Pooling Attention for Background Structure Modeling, which together couple a new network architecture, dubbed as D2P-BMNet, which significantly improves over prior methods across all benchmarks and maintains excellent inference speeds.
- DEHRFormer: Real-Time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes18
A real-time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer) and a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing.
- CPLFormer: Cross-scale Prototype Learning Transformer for Image Snow Removal17
This work proposes a novel approach, CPLFormer, which uses snow prototypes to own comprehensive clean scene understanding through learning from cross-scale features, outperforming convolutional network and vanilla transformer-based solutions.
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- PromptHaze: Prompting Real-world Dehazing via Depth Anything Model10
Extensive experiments on widely-used real-world dehazing benchmarks demonstrate the superiority of PromptHaze in recovering authentic backgrounds and fine details from various haze scenes, outperforming state-of-the-art methods across multiple quality metrics.
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- Sequential Affinity Learning for Video Restoration10
The proposed Sequential Affinity Learning Network (SALN), a novel approach for video restoration that eliminates inefficient temporal modeling operators and pixel-level feature alignment in the network architecture, is designed based on an affinity mechanism that establishes direct correspondences between the Query frame, degraded sequence, and restored frames in latent space.
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- RSFDM-Net: Real-Time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement9
This work proposes a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) and introduces a Three-branch Feature Extraction (TFE) block in the primary net that processes images pixel by pixel to integrate the color information extended by the same channel.
- Role of identified noncoding RNA in erectile dysfunction9
The mechanisms and functions of identified ncRNAs that are associated withrectile dysfunction are summed up in this review.
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- Towards Efficient Single Image Dehazing and Desnowing7
An efficient and compact image restoration network named DAN-Net (Degradation-Adaptive Neural Network) to address the problem of removing adverse weather conditions from images, which consists of multiple compact expert networks with one adaptive gated neural.
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- Farther Than Mirror: Explore Pattern-Compensated Depth of Mirror with Temporal Changes for Video Mirror Detection6
A novel framework called FTM-Net is devised, which includes two main contributions: a Pattern-Compensated DOM estimation strategy and a Dual-Granularity Affinity module that can effectively detect mirrors in video sequences by tracking and integrating DOM changes across video frames.
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- Unique Substrate Recognition and Sodium–Substrate Binding Stoichiometry in a Bacterial Serotonin Transporter, TuriSERT3
The present study demonstrated the existence of a unique substrate binding site and 1:1 stoichiometry of sodium–substrate binding in TuriSERT, a novel structural finding for the NSS transporters.
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- Retroperitoneal laparoscopic renorrhaphy for post-percutaneous nephrolithotomy hemorrhage: a case report2
It is suggested that retroperitoneal laparoscopic renorrhaphy can be effective in patients who have undergone failed renal arterial embolization or are reluctant to undergo renal arterials embolizing.
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- A Conserved Intramolecular Ion-Pair Plays a Critical but Divergent Role in Regulation of Dimerization and Transport Function among the Monoamine Transporters1
It is proposed that the transport function is modulated by the equilibrium between monomers and dimers on the cell surface, which is regulated by a potential compensatory mechanism but with different molecular solutions among the monoamine transporters.
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- A review of on-line monitoring technology of oil chromatography based on gas chromatography analysis1
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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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