EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
Dataset distillation aims to synthesize a compact dataset from the original large-scale one, enabling highly efficient learning while preserving competitive model performance. However, traditional techniques primarily capture low-level visual features, neglecting the high-level semantic and structural information inherent in images. In this paper, we propose EDITS, a novel framework that exploits the implicit textual semantics within the image data to achieve enhanced distillation. First, external texts generated by a Vision Language Model (VLM) are fused with image features through a Global Semantic Query module, forming the prior clustered buffer. Local Semantic Awareness then selects representative samples from the buffer to construct image and text prototypes, with the latter produced by guiding a Large Language Model (LLM) with meticulously crafted prompt. Ultimately, Dual Prototype Guidance strategy generates the final synthetic dataset through a diffusion model. Extensive experiments confirm the effectiveness of our method.Source code is available in: https://github.com/einsteinxia/EDITS.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DreamHOI: Subject-Driven Generation of 3D Human-Object Interactions with Diffusion Priors
We present DreamHOI, a novel method for zero-shot synthesis of human-object interactions (HOIs), enabling a 3D human model to realistically interact with any given object based on a textual description. This task is comp…
Human-Object Interaction DetectionNeRFLEDITS++: Limitless Image Editing using Text-to-Image Models
Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their …
Image ManipulationImage-Editing Specialists: An RLAIF Approach for Diffusion Models
We present a novel approach to training specialized instruction-based image-editing diffusion models, addressing key challenges in structural preservation with input images and semantic alignment with user prompts. We in…
X-Edit: Detecting and Localizing Edits in Images Altered by Text-Guided Diffusion Models
Text-guided diffusion models have significantly advanced image editing, enabling highly realistic and local modifications based on textual prompts. While these developments expand creative possibilities, their malicious …
Face SwappingSSIMNeuSEditor: From Multi-View Images to Text-Guided Neural Surface Edits
Implicit surface representations are valued for their compactness and continuity, but they pose significant challenges for editing. Despite recent advancements, existing methods often fail to preserve identity and mainta…