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- DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes151
DeVRF, a novel representation to accelerate learning dynamic radiance fields via utilizing the 3D volumetric canonical space learnt from multi-view static images to ease the learning of 4D voxel deformation field with only few-view dynamic sequences is presented.
- HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation138
This work presents HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm through a novel heterogeneous low-rank adaptation technique and a tailored hierarchical visual perception approach.
- BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation138
A novel semi-supervised learning framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the potential of medical image SSL models and the experimental results verify the framework's effectiveness with a significant improvement over various state-of-the-art methods.
- VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM93
The VideoRefer Suite is introduced to empower Video LLM for finer-level spatial-temporal video understanding, i.e., enabling perception and reasoning on any objects throughout the video, and comprehensively assess the spatial-temporal understanding capability of a Video LLM.
- DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization72
It is argued that the computational cost brought by fine-tuning can be rather unnecessary, and a novel perspective to improving DMG without increasing computational cost is presented, i.e., device-specific parameter generation which directly maps data distribution to parameters.
- Consensus Graph Representation Learning for Better Grounded Image Captioning64
The Consensus Rraph Representation Learning framework (CGRL) is proposed for GIC that incorporates a consensus representation into the grounded captioning pipeline that can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further.
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- Intelligent Model Update Strategy for Sequential Recommendation55
IntellectReq is a novel learning task, aimed at the detection of out-of-distribution data, thereby fine-tuning adaptive communication strategies and evidencing a marked improvement in the efficiency and generalizability of edge-cloud collaborative and dynamic recommendation systems.
- MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-Based Image Captioning52
This work proposes the Multimodal relAtional Graph adversarIal InferenCe (MAGIC) framework, a framework that can adaptively construct multiple multimodal relational graphs of images and model complex relationships among graphs to represent descriptive diversity and validate the effectiveness of MAGIC in generating diverse captions from different relational information items of an image.
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- 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.
- Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels40
This work introduces a novel task, Partial labeling and Long-Tailed Multi-Label Classification (PLT-MLC), and proposes an end-to-end learning framework, COMIC, which significantly outperforms general MLC, LT-MLC and PL-MLC methods in terms of effectiveness and robustness on newly created PLT-MLC datasets.
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- Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI36
A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.
- Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation35
The Device-Cloud LLM-SRM Collaborative Recommendation Framework (LSC4Rec) is designed under a device-cloud collaboration setting and aims to integrate the advantages of both LLMs and SRMs, as well as the benefits of cloud and edge computing, achieving a complementary synergy.
- HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding35
This paper introduces the weakly-supervised contrastive learning that classifies the video as action-consistent and action-independent frames relying on the video-caption action semantic correspondence and designs the hierarchical reasoning layers to decouple fully connected multi-head attention and remove the redundant interfering correlations.
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- EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model30
The Eyecare Kit is proposed, which systematically tackles three key challenges of intelligent ophthalmic diagnosis with the tailored dataset, benchmark and model, and develops the EyecareGPT, optimized for fine-grained ophthalmic visual understanding thoroughly.
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- METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection30
METER is introduced, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD that significantly outperforms existing OAD approaches in various application scenarios and employs a novel dynamic concept adaptation technique that leverages a hypernetwork to dynamically generate the parameter shift of the base detection model.
- Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding28
A variational cross-graph reasoning framework is proposed that explicitly decomposes video and language into hierarchical semantic graphs, respectively, and learns fine-grained semantic correspondence between the two graphs, and introduces a novel adaptive structured semantics learning approach to derive the structure-informed and domain-generalizable graph representations.
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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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