Textual Inversion learns a single embedding vector from a few images to represent personal concepts inside the text embedding space of a frozen text-to-image model, enabling their composition in natural language prompts.
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Learning to prompt for vision-language models.Int
9 Pith papers cite this work, alongside 2,607 external citations. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
TB-AVA uses text-mediated gated semantic modulation to enable efficient audio-visual alignment, achieving state-of-the-art results on AVE, AVS, and AVVP benchmarks.
FACTOR uses counterfactual image perturbations to quantify and suppress attribute-dependent predictions in open-vocabulary object detection, improving robustness on corrupted datasets without any training.
GeoStack composes multiple domain experts into VLMs with preserved base knowledge and O(1) inference time via geometric stacking and a weight-folding property.
A new joint spatio-temporal enlargement model for micro-video popularity prediction using frame scoring for long sequences and a topology-aware memory bank for unbounded historical associations.
VANGUARD is a staged-training VLM framework that reports 94% ROC-AUC and 84% F1 on UCF-Crime while adding chain-of-thought reasoning and spatial grounding to video anomaly detection.
Grad-ECLIP is an equivalent but flawed variant of attention-based interpretation, with two principles proposed to ensure model explanations reflect the original model.
ProtoCLIP improves zero-shot chest X-ray classification in CLIP models by 2-10 AUC points via curated data and prototype-aligned distillation, reaching 0.94 AUC for pneumothorax on VinDr-CXR.
citing papers explorer
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An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Textual Inversion learns a single embedding vector from a few images to represent personal concepts inside the text embedding space of a frozen text-to-image model, enabling their composition in natural language prompts.
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Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
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TB-AVA: Text as a Semantic Bridge for Audio-Visual Parameter Efficient Finetuning
TB-AVA uses text-mediated gated semantic modulation to enable efficient audio-visual alignment, achieving state-of-the-art results on AVE, AVS, and AVVP benchmarks.
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FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection
FACTOR uses counterfactual image perturbations to quantify and suppress attribute-dependent predictions in open-vocabulary object detection, improving robustness on corrupted datasets without any training.
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GeoStack: A Framework for Quasi-Abelian Knowledge Composition in VLMs
GeoStack composes multiple domain experts into VLMs with preserved base knowledge and O(1) inference time via geometric stacking and a weight-folding property.
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Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction
A new joint spatio-temporal enlargement model for micro-video popularity prediction using frame scoring for long sequences and a topology-aware memory bank for unbounded historical associations.
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Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models
VANGUARD is a staged-training VLM framework that reports 94% ROC-AUC and 84% F1 on UCF-Crime while adding chain-of-thought reasoning and spatial grounding to video anomaly detection.
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Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation
Grad-ECLIP is an equivalent but flawed variant of attention-based interpretation, with two principles proposed to ensure model explanations reflect the original model.
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ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification
ProtoCLIP improves zero-shot chest X-ray classification in CLIP models by 2-10 AUC points via curated data and prototype-aligned distillation, reaching 0.94 AUC for pneumothorax on VinDr-CXR.