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- A Predictive Data Feature Exploration-Based Air Quality Prediction Approach143
An improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h is proposed and the experimental results show that the proposed method is superior to other schemes and proves the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis.
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- EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks67
A new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg) is provided, which can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer.
- Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models41
A novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data.
- A comprehensive survey of intestine histopathological image analysis using machine vision approaches34
This investigation conducts a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology and provides a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine.
- STEP: Enhancing Video-LLMs’ Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training33
STEP, a novel graph-guided self-training method that enables Video-LLMs to generate reasoning-rich fine-tuning data from any raw videos to improve itself, achieves superior performance with a minimal amount of self-generated rationale-enriched training samples in both compositional reasoning and comprehensive understanding benchmarks, highlighting the broad applicability and vast potential.
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- On Path to Multimodal Generalist: General-Level and General-Bench29
This project introduces General-Level, an evaluation framework that defines 5-scale levels of MLLM performance and generality, offering a methodology to compare MLLMs and gauge the progress of existing systems towards more robust multimodal generalists and, ultimately, towards AGI.
- Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program15
Experimental results demonstrate that SVIP-Reward improves MLLM performance across training and inference-time scaling, yielding better results on benchmarks while reducing hallucinations and enhancing reasoning ability.
- De-fine: De composing and Re fin ing Visual Programs with Auto-Feedback14
Inspired by benders decomposition, this work introduces De-fine, a training-free framework that automatically decomposes complex tasks into simpler subtasks and refines programs through auto-feedback, which can improve logical reasoning performance by integrating the strengths of multiple models.
- Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales12
Fact is introduced, a novel paradigm designed to generate multimodal rationales that are faithful, concise, and transferable for teaching MLLMs and filter rationales that can be transferred to end-to-end paradigms from programming paradigms to guarantee transferability.
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- What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities10
OmniBench is introduced, a self-generating, cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition and OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.
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- Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms9
This paper aims to bridge the theoretical and practical aspects of GVAs, concluding those that operate in environments closely mirroring the real world are more likely to demonstrate human-like intelligence.
- Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining8
Iris is introduced, a foundational visual agent that addresses challenges in visual perception through two key innovations: Information-Sensitive Cropping (ISC) and Self-Refining Dual Learning (SRDL).
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- Counterfactual Evolution of Multimodal Datasets via Visual Programming1
Experiments show that SCOPE improves reasoning performance, exposes model blind spots, and enhances visual dialog capabilities, and is proposed to support training, a curriculum learning strategy that aligns model capacity with sample difficulty.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. 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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