Diffusion Feedback Helps CLIP See Better
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Wenxuan, Sun, Quan, Zhang, Fan, Tang, Yepeng, Liu, Jing, Wang, Xinlong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Image Difference Grounding with Natural Language
by: Wang, Wenxuan, et al.
Published: (2025)
by: Wang, Wenxuan, et al.
Published: (2025)
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
by: Sun, Quan, et al.
Published: (2024)
by: Sun, Quan, et al.
Published: (2024)
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
by: Liu, Jing, et al.
Published: (2025)
by: Liu, Jing, et al.
Published: (2025)
ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better
by: Nath, Mriganka, et al.
Published: (2026)
by: Nath, Mriganka, et al.
Published: (2026)
Can Graphs Help Vision SSMs See Better?
by: Parikh, Dhruv, et al.
Published: (2026)
by: Parikh, Dhruv, et al.
Published: (2026)
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
by: Xue, Leyan, et al.
Published: (2025)
by: Xue, Leyan, et al.
Published: (2025)
CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation
by: Zhang, Dengke, et al.
Published: (2024)
by: Zhang, Dengke, et al.
Published: (2024)
End-to-End Vision Tokenizer Tuning
by: Wang, Wenxuan, et al.
Published: (2025)
by: Wang, Wenxuan, et al.
Published: (2025)
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
by: Wang, Wenxuan, et al.
Published: (2024)
by: Wang, Wenxuan, et al.
Published: (2024)
Unveiling Parts Beyond Objects:Towards Finer-Granularity Referring Expression Segmentation
by: Wang, Wenxuan, et al.
Published: (2023)
by: Wang, Wenxuan, et al.
Published: (2023)
CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP
by: Tang, Zhenchen, et al.
Published: (2024)
by: Tang, Zhenchen, et al.
Published: (2024)
Uniform Discrete Diffusion with Metric Path for Video Generation
by: Deng, Haoge, et al.
Published: (2025)
by: Deng, Haoge, et al.
Published: (2025)
DCP-CLIP:A Coarse-to-Fine Framework for Open-Vocabulary Semantic Segmentation with Dual Interaction
by: Wang, Jing, et al.
Published: (2026)
by: Wang, Jing, et al.
Published: (2026)
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
by: Pei, Gensheng, et al.
Published: (2025)
by: Pei, Gensheng, et al.
Published: (2025)
BrainMCLIP: Brain Image Decoding with Multi-Layer feature Fusion of CLIP
by: Xia, Tian, et al.
Published: (2025)
by: Xia, Tian, et al.
Published: (2025)
Anomize: Better Open Vocabulary Video Anomaly Detection
by: Li, Fei, et al.
Published: (2025)
by: Li, Fei, et al.
Published: (2025)
You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
by: Ma, Baorui, et al.
Published: (2024)
by: Ma, Baorui, et al.
Published: (2024)
CLIP-Map: Structured Matrix Mapping for Parameter-Efficient CLIP Compression
by: Zhang, Kangjie, et al.
Published: (2026)
by: Zhang, Kangjie, et al.
Published: (2026)
CLIP Brings Better Features to Visual Aesthetics Learners
by: Xu, Liwu, et al.
Published: (2023)
by: Xu, Liwu, et al.
Published: (2023)
IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
by: Wang, Juan, et al.
Published: (2026)
by: Wang, Juan, et al.
Published: (2026)
HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration
by: Huang, Yushi, et al.
Published: (2024)
by: Huang, Yushi, et al.
Published: (2024)
See More, Change Less: Anatomy-Aware Diffusion for Contrast Enhancement
by: Liu, Junqi, et al.
Published: (2025)
by: Liu, Junqi, et al.
Published: (2025)
GPT4Image: Large Pre-trained Models Help Vision Models Learn Better on Perception Task
by: Ding, Ning, et al.
Published: (2023)
by: Ding, Ning, et al.
Published: (2023)
Emu: Generative Pretraining in Multimodality
by: Sun, Quan, et al.
Published: (2023)
by: Sun, Quan, et al.
Published: (2023)
Mixup Helps Understanding Multimodal Video Better
by: Ma, Xiaoyu, et al.
Published: (2025)
by: Ma, Xiaoyu, et al.
Published: (2025)
CLIP-AE: CLIP-assisted Cross-view Audio-Visual Enhancement for Unsupervised Temporal Action Localization
by: Xia, Rui, et al.
Published: (2025)
by: Xia, Rui, et al.
Published: (2025)
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
by: Gao, Peng, et al.
Published: (2021)
by: Gao, Peng, et al.
Published: (2021)
Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation
by: Zhang, Xiaoyu, et al.
Published: (2024)
by: Zhang, Xiaoyu, et al.
Published: (2024)
AI Sees Your Location, But With A Bias Toward The Wealthy World
by: Huang, Jingyuan, et al.
Published: (2025)
by: Huang, Jingyuan, et al.
Published: (2025)
See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs
by: Dai, Ziyun, et al.
Published: (2025)
by: Dai, Ziyun, et al.
Published: (2025)
DiCLIP: Diffusion Model Enhances CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
by: Yang, Zhiwei, et al.
Published: (2026)
by: Yang, Zhiwei, et al.
Published: (2026)
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
by: Wang, Dianyi, et al.
Published: (2025)
by: Wang, Dianyi, et al.
Published: (2025)
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
by: Shen, Sitian, et al.
Published: (2023)
by: Shen, Sitian, et al.
Published: (2023)
Long-CLIP: Unlocking the Long-Text Capability of CLIP
by: Zhang, Beichen, et al.
Published: (2024)
by: Zhang, Beichen, et al.
Published: (2024)
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models
by: Wang, Fu-Yun, et al.
Published: (2025)
by: Wang, Fu-Yun, et al.
Published: (2025)
FIX-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Text
by: Wang, Bingchao, et al.
Published: (2025)
by: Wang, Bingchao, et al.
Published: (2025)
Generative Multimodal Models are In-Context Learners
by: Sun, Quan, et al.
Published: (2023)
by: Sun, Quan, et al.
Published: (2023)
EVA-02: A Visual Representation for Neon Genesis
by: Fang, Yuxin, et al.
Published: (2023)
by: Fang, Yuxin, et al.
Published: (2023)
Bridging the Gap: Aligning Text-to-Image Diffusion Models with Specific Feedback
by: Niu, Xuexiang, et al.
Published: (2024)
by: Niu, Xuexiang, et al.
Published: (2024)
Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
by: Zhu, Muzhi, et al.
Published: (2024)
by: Zhu, Muzhi, et al.
Published: (2024)
Similar Items
-
Image Difference Grounding with Natural Language
by: Wang, Wenxuan, et al.
Published: (2025) -
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
by: Sun, Quan, et al.
Published: (2024) -
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
by: Liu, Jing, et al.
Published: (2025) -
ClipTTT: CLIP-Guided Test-Time Training Helps LVLMs See Better
by: Nath, Mriganka, et al.
Published: (2026) -
Can Graphs Help Vision SSMs See Better?
by: Parikh, Dhruv, et al.
Published: (2026)