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- Graph Retrieval-Augmented Generation: A Survey647
This article provides the first comprehensive overview of GraphRAG methodologies, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation, and outlines the core technologies and training methods at each stage.
- NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models447
This work proposes NaturalSpeech 3, a TTS system with novel factorized diffusion models to generate natural speech in a zero-shot way that outperforms the state-of-the-art TTS systems on quality, similarity, prosody, and intelligibility, and achieves on-par quality with human recordings.
- AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea261
AnyEdit is presented, a comprehensive multi-modal instruction editing dataset, comprising 2.5 million high-quality editing pairs spanning over 20 editing types and five domains, and shows that AnyEdit consistently boosts the performance of diffusion-based editing models.
- Rethinking the Bottom-Up Framework for Query-Based Video Localization189
It is argued that the performance of bottom-up framework is severely underestimated by current unreasonable designs, including both the backbone and head network, and designed a novel top-up model: Graph-FPN with Dense Predictions (GDP).
- HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data167
This work presents a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm, and uses this framework to identify and eliminate hallucinations in the training data automatically, and indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations.
- Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning156
The proposed Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks, is designed and trained on Moment-10M, a large-scale video instruction dataset with segment-level instruction data, enabling it to perform segment-level reasoning and localization.
- 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.
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- Fine-Grained Semantically Aligned Vision-Language Pre-Training111
This paper introduces LOUPE, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine- grained semantic alignment from the novel perspective of game-theoretic interactions, and proposes an uncertainty-aware neural Shapley interaction learning module.
- NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results106
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results, based on the SIDD benchmark.
- Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning105
A variational cross-graph reasoning framework that explicitly decomposes video and language into multiple structured hierarchies and learns fine-grained semantic correspondence among them is proposed and empirically finds that they fail to generalize to queries with novel combinations of seen words.
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- A Study on ReLU and Softmax in Transformer94
A full ReLU architecture named ReLUFormer is proposed which performs better than the baseline Transformer on long sequence tasks such as document translation and FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key- value memories.
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- WorldGPT: Empowering LLM as Multimodal World Model80
WorldGPT is introduced, a generalist world model built upon Multimodal Large Language Model (MLLM) that acquires an understanding of world dynamics through analyzing millions of videos across various domains and is integrated with a novel cognitive architecture that combines memory offloading, knowledge retrieval, and context reflection.
- Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation80
This paper proposes a novel unsupervised reinforcement learning approach to learn transferable meta-skills from unannotated environments without any supervisory signals, and shows that this method learns transferable motor primitives for visual navigation.
- GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs79
This work generates and curates large-scale graph-summary pair data with the assistance of LLMs, and introduces a novel graph-summary pretraining method, combined with invariant learning, to enhance graph foundation models with strong cross-domain zero-shot transferability.
- Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction73
This paper proposes to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor and employs the sentence-level selective attention to reduce the effect of noisy or mismatched sentences.
- Learning Dynamic Context Augmentation for Global Entity Linking72
A simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document, and can cope with different local EL models as a plug-and-enhance module.
- Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis68
A sequential-level deep learning model is proposed to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images through the preservation of spatial structures and disentangle the informative features for clinical image classification of infectious keratitis.
- Learning Hybrid Behavior Patterns for Multimedia Recommendation65
A Hybrid Clustering Graph Convolutional Network (HCGCN) is proposed for multimedia recommendation that performs high-order graph convolutions inside user- item clusters and item-item clusters to capture various user behavior patterns and designs corresponding clustering losses to enhance user-item preference feedback and multimodal representation learning constraint to adjust the modality importance.
- CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction64
This paper proposes a novel contrastive instance learning (CIL) framework, which regards the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance.
- 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.
- Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World63
A Cross-modal prediCate boosting (CaCao) framework, where a visually-prompted language model is learned to generate diverse fine-grained predicates in a low-resource way for open-world predicate scene graph generation (Epic).
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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