# Unveiling Glitches: A Deep Dive into Image Encoding Bugs within CLIP

arXiv: https://arxiv.org/abs/2407.00592 · HTML: https://mlsanity.com/paper/2407.00592/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 12 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://mlsanity.com/paper/2104.06644/) (2021)
- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning](https://mlsanity.com/paper/2208.08831/) (2022)
- [Mass-Producing Failures of Multimodal Systems with Language Models](https://mlsanity.com/paper/2306.12105/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhancing Performance of Explainable AI Models with Constrained Concept Refinement](https://mlsanity.com/paper/2502.06775/) (2025)

## Abstract (arXiv)

Understanding the limitations and weaknesses of state-of-the-art models in artificial intelligence is crucial for their improvement and responsible application. In this research, we focus on CLIP, a model renowned for its integration of vision and language processing. Our objective is to uncover recurring problems and blind spots in CLIP's image comprehension. By delving into both the commonalities and disparities between CLIP and human image understanding, we augment our comprehension of these models' capabilities. Through our analysis, we reveal significant discrepancies in CLIP's interpretation of images compared to human perception, shedding light on areas requiring improvement. Our methodologies, the Discrepancy Analysis Framework (DAF) and the Transformative Caption Analysis for CLIP (TCAC), enable a comprehensive evaluation of CLIP's performance. We identify 14 systemic faults, including Action vs. Stillness confusion, Failure to identify the direction of movement or positioning of objects in the image, Hallucination of Water-like Features, Misattribution of Geographic Context, among others. By addressing these limitations, we lay the groundwork for the development of more accurate and nuanced image embedding models, contributing to advancements in artificial intelligence.