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- LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning290
LL3DA is presented, a Large Language 3D Assistant that takes point cloud as the direct input and responds to both text instructions and visual interactions that enables LMMs to better comprehend human interactions with the 3D environment and further remove the ambiguities within plain texts.
- Vision-model-based impairment metric to evaluate blocking artifacts in digital video136
Investigations are conducted to simplify and refine a vision-model-based video quality metric without compromising its prediction accuracy and the results show a strong correlation between the objective blocking ratings and the mean opinion scores on blocking artifacts.
- CTransCNN: Combining transformer and CNN in multilabel medical image classification120
The framework developed exhibits strong competitiveness compared to previous research and its robust generalization ability makes it applicable to other medical multilabel image classification tasks.
- FECANet: Boosting Few-Shot Semantic Segmentation With Feature-Enhanced Context-Aware Network109
A feature enhancement module is proposed to suppress the matching noise caused by inter-class local similarity and enhance the intra-class relevance in the naive correlation, and a novel correlation reconstruction module is proposed that encodes extra correspondence relations between foreground and background and multi-scale context semantic features.
- Scene text extraction based on edges and support vector regression100
This paper presents a scene text extraction technique that automatically detects and segments texts from scene images by using a support vector regression model that is trained using bags-of-words representation.
- MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer77
A novel framework named Multimodal Alignment-Guided Dynamic Token Pruning (MADTP) for accelerating various VLTs is proposed, which significantly reduces the computational complexity of kinds of multimodal models while preserving competitive performance.
- Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation71
A saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes and an object guided label refinement module to take a full use of both the segmentation prediction and the initial labels for obtaining superior pseudo-labels.
- Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation60
This paper proposes to leverage the multi-class label information during the episodic training to encourage the network to generate more semantically meaningful features for each category and proposes a fused prototype guidance branch for the segmentation of the query image.
- Spatial Structure Constraints for Weakly Supervised Semantic Segmentation51
A CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content, and an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency are proposed.
- Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation50
This work proposes an end-to-end multi-granularity denoising and bidirectional alignment model, to alleviate the noisy label and multi-class generalization issues, and proposes the online noise filtering and progressive noise detection modules to tackle image-level and pixel-level noise.
- IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models44
This study introduces IQAGPT, an innovative computed tomography IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports and demonstrates the feasibility of assessing image quality using LLMs.
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- Towards an end-to-end artificial intelligence driven global weather forecasting system37
It is demonstrated that Adas can assimilate global conventional observations to produce high-quality analysis, enabling the system to operate stably for long term, and can generate accurate end-to-end weather forecasts with comparable skill to those of the IFS, demonstrating the promising potential of data-driven approaches.
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- VideoMAC: Video Masked Autoencoders Meet ConvNets33
This paper proposes a new approach termed as VideoMAC, which combines video masked autoencoders with resource-friendly Con-vNets, and demonstrates that VideoMAC, empowering classical / modern convolutional encoders to harness the benefits of MVM, outperforms ViT-based approaches on downstream tasks.
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- Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images24
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- Hyperscale Hardware Optimized Neural Architecture Search22
This paper introduces the first Hyperscale Hardware Optimized Neural Architecture Search (H2O-NAS) to automatically design accurate and performant machine learning models tailored to the underlying hardware architecture to facilitate a smooth and automated transition from research to production.
- A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms22
A computer-aided diagnosis system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms is built and offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring.
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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 2026-10-14. 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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