Vision Transformers Don't Need Trained Registers
Fuente:
arXiv
Saved in:
| Main Authors: | Jiang, Nick, Dravid, Amil, Efros, Alexei, Gandelsman, Yossi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Interpreting CLIP's Image Representation via Text-Based Decomposition
by: Gandelsman, Yossi, et al.
Published: (2023)
by: Gandelsman, Yossi, et al.
Published: (2023)
Synthesizing Moving People with 3D Control
by: Li, Boyi, et al.
Published: (2024)
by: Li, Boyi, et al.
Published: (2024)
Interpreting the Second-Order Effects of Neurons in CLIP
by: Gandelsman, Yossi, et al.
Published: (2024)
by: Gandelsman, Yossi, et al.
Published: (2024)
Interpreting the Weight Space of Customized Diffusion Models
by: Dravid, Amil, et al.
Published: (2024)
by: Dravid, Amil, et al.
Published: (2024)
Interpreting ResNet-based CLIP via Neuron-Attention Decomposition
by: Bu, Edmund, et al.
Published: (2025)
by: Bu, Edmund, et al.
Published: (2025)
Quantifying and Enabling the Interpretability of CLIP-like Models
by: Madasu, Avinash, et al.
Published: (2024)
by: Madasu, Avinash, et al.
Published: (2024)
Jailbreaking Vision-Language Models Through the Visual Modality
by: Azulay, Aharon, et al.
Published: (2026)
by: Azulay, Aharon, et al.
Published: (2026)
Test-Time Training on Video Streams
by: Wang, Renhao, et al.
Published: (2023)
by: Wang, Renhao, et al.
Published: (2023)
Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations
by: Jiang, Nick, et al.
Published: (2024)
by: Jiang, Nick, et al.
Published: (2024)
Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
by: Woo, Sangmin, et al.
Published: (2024)
by: Woo, Sangmin, et al.
Published: (2024)
An Empirical Study of Autoregressive Pre-training from Videos
by: Rajasegaran, Jathushan, et al.
Published: (2025)
by: Rajasegaran, Jathushan, et al.
Published: (2025)
You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
by: Zhao, Kairan, et al.
Published: (2026)
by: Zhao, Kairan, et al.
Published: (2026)
Large Pre-Training Datasets Don't Always Guarantee Robustness after Fine-Tuning
by: Hwang, Jaedong, et al.
Published: (2024)
by: Hwang, Jaedong, et al.
Published: (2024)
Don't Deceive Me: Mitigating Gaslighting through Attention Reallocation in LMMs
by: Jiao, Pengkun, et al.
Published: (2025)
by: Jiao, Pengkun, et al.
Published: (2025)
Don't Fear Peculiar Activation Functions: EUAF and Beyond
by: Wang, Qianchao, et al.
Published: (2024)
by: Wang, Qianchao, et al.
Published: (2024)
LLMs can see and hear without any training
by: Ashutosh, Kumar, et al.
Published: (2025)
by: Ashutosh, Kumar, et al.
Published: (2025)
Diffusion Models as Data Mining Tools
by: Siglidis, Ioannis, et al.
Published: (2024)
by: Siglidis, Ioannis, et al.
Published: (2024)
The Unreasonable Effectiveness of Text Embedding Interpolation for Continuous Image Steering
by: Ekin, Yigit, et al.
Published: (2026)
by: Ekin, Yigit, et al.
Published: (2026)
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
by: Yu, Zhuoran, et al.
Published: (2023)
by: Yu, Zhuoran, et al.
Published: (2023)
Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement
by: Nzoyem, Roussel Desmond, et al.
Published: (2026)
by: Nzoyem, Roussel Desmond, et al.
Published: (2026)
Vision Transformers Need Registers
by: Darcet, Timothée, et al.
Published: (2023)
by: Darcet, Timothée, et al.
Published: (2023)
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
by: Koepke, A. Sophia, et al.
Published: (2026)
by: Koepke, A. Sophia, et al.
Published: (2026)
Focus, Don't Prune: Identifying Instruction-Relevant Regions for Information-Rich Image Understanding
by: Kwon, Mincheol, et al.
Published: (2026)
by: Kwon, Mincheol, et al.
Published: (2026)
Don't Play Favorites: Minority Guidance for Diffusion Models
by: Um, Soobin, et al.
Published: (2023)
by: Um, Soobin, et al.
Published: (2023)
When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection
by: Goodge, Adam, et al.
Published: (2024)
by: Goodge, Adam, et al.
Published: (2024)
Multi-Token Prediction Needs Registers
by: Gerontopoulos, Anastasios, et al.
Published: (2025)
by: Gerontopoulos, Anastasios, et al.
Published: (2025)
Unsupervised Training of Vision Transformers with Synthetic Negatives
by: Giakoumoglou, Nikolaos, et al.
Published: (2025)
by: Giakoumoglou, Nikolaos, et al.
Published: (2025)
Don't Judge by the Look: Towards Motion Coherent Video Representation
by: Zhang, Yitian, et al.
Published: (2024)
by: Zhang, Yitian, et al.
Published: (2024)
World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertainty
by: Mei, Zhiting, et al.
Published: (2025)
by: Mei, Zhiting, et al.
Published: (2025)
Learning Video Representations without Natural Videos
by: Yu, Xueyang, et al.
Published: (2024)
by: Yu, Xueyang, et al.
Published: (2024)
Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos
by: Kalluri, Tarun, et al.
Published: (2024)
by: Kalluri, Tarun, et al.
Published: (2024)
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing Imagery
by: Dahal, Ashim, et al.
Published: (2024)
by: Dahal, Ashim, et al.
Published: (2024)
Vision Transformers Need More Than Registers
by: Shi, Cheng, et al.
Published: (2026)
by: Shi, Cheng, et al.
Published: (2026)
Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions
by: Parmar, Mihir, et al.
Published: (2022)
by: Parmar, Mihir, et al.
Published: (2022)
I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging
by: Yadav, Nand Kumar, et al.
Published: (2025)
by: Yadav, Nand Kumar, et al.
Published: (2025)
Don't Fight Hallucinations, Use Them: Estimating Image Realism using NLI over Atomic Facts
by: Rykov, Elisei, et al.
Published: (2025)
by: Rykov, Elisei, et al.
Published: (2025)
Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers
by: Wen, Yuxin, et al.
Published: (2024)
by: Wen, Yuxin, et al.
Published: (2024)
Steering CLIP's vision transformer with sparse autoencoders
by: Joseph, Sonia, et al.
Published: (2025)
by: Joseph, Sonia, et al.
Published: (2025)
Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
by: Loeffler, Christoffer, et al.
Published: (2022)
by: Loeffler, Christoffer, et al.
Published: (2022)
Driving on Registers
by: Kirby, Ellington, et al.
Published: (2026)
by: Kirby, Ellington, et al.
Published: (2026)
Similar Items
-
Interpreting CLIP's Image Representation via Text-Based Decomposition
by: Gandelsman, Yossi, et al.
Published: (2023) -
Synthesizing Moving People with 3D Control
by: Li, Boyi, et al.
Published: (2024) -
Interpreting the Second-Order Effects of Neurons in CLIP
by: Gandelsman, Yossi, et al.
Published: (2024) -
Interpreting the Weight Space of Customized Diffusion Models
by: Dravid, Amil, et al.
Published: (2024) -
Interpreting ResNet-based CLIP via Neuron-Attention Decomposition
by: Bu, Edmund, et al.
Published: (2025)