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- 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.
- 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.
- 3D motion retrieval with motion index tree167
A novel content-based 3D motion retrieval algorithm is presented that partition the motion library and construct a motion index tree based on a hierarchical motion description and serves as a classifier to determine the sub-library that contains the promising similar motions to the query sample.
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- Deep Convolutional Neural Networks with Merge-and-Run Mappings70
This work shows that the merge-and-run mapping is a linear idempotent function in which the transformation matrix is idem Potent, and thus improves information flow, making training easy.
- InstructVid2Vid: Controllable Video Editing with Natural Language Instructions56
Experimental results demonstrate that InstructVid2Vid is capable of generating high-quality, temporally coherent videos and performing diverse edits, including attribute editing, background changes, and style transfer, and underscores the versatility and effectiveness of the proposed method.
- 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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- 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.
- Real-Time Driving Scene Semantic Segmentation33
Object Weighted Focal Loss (OWFL) is proposed to deal with the serious class imbalance issue in pixel-wise segmentation task and could effectively improve the overall mean IoU of minor and hard objects by increasing loss contribution from them.
- 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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- Improving Vision Anomaly Detection With the Guidance of Language Modality12
This paper proposes Cross-modal Guidance (CMG), comprising of Cross-modal Entropy Reduction (CMER) and Cross-modal Linear Embedding (CMLE), to address the issues of redundant information and sparse latent space, respectively.
- Adaptive control in cartoon data reusing11
A novel approach, which reuses Traditional Chinese Cartoon to create new animations, utilizes image processing, computer vision, and machine learning in cartoon creation and the experiment results demonstrate that the animation's quality can be effectively improved by the fusion of these techniques.
- 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.
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- Annotating web images using NOVA6
A new sparsity-based approach NOVA (NOn-conVex group spArsity) is proposed, which is the first to introduce non-convex penalty for group selection in high-dimensional heterogeneous features space and achieves the consistency.
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- Ask Questions With Double Hints: Visual Question Generation With Answer-Awareness and Region-Reference5
A new double-hints guided Graph-to-Sequence learning framework is proposed, which first models them as a dynamic graph and learns the implicit topology end-to-end, and then utilizes a graph-to-sequence model to generate the questions with double hints.
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- SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness2
This paper proposes SpatialFusion, a novel framework that internalizes 3D geometric awareness into unified image generation models and significantly enhances performance on spatially-aware benchmarks, notably outperforming leading models such as GPT-4o.
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- TRIDENT: Efficient Small-Large Model Collaboration via Heterogeneous Expert Decoupling–
This paper proposes Tri-gate Routing for Inference via Decoupled Efficient Network Technologies (TRIDENT), a highly efficient and robust heterogeneous collaborative inference framework that provides a robust and interpretable pathway for efficient collaborative intelligence.
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- Generalized Visual Relation Detection With Diffusion Models–
Benefiting from the diffusion-based generative process, the Diff-VRD is able to generate visual relations beyond the pre-defined category labels of datasets, and is able to generate visual relations beyond the pre-defined category labels of datasets.
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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 2026-10-13. 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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