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Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study

Reza Abbasi, Ali Nazari, Aminreza Sefid, Mohammadali Banayeeanzade, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah

TL;DR

This study analyzes CLIP's limitations in multi-object scenes using two controlled datasets, SimCO and ComCO. It separately evaluates the image encoder (IOC, IOR) and the text encoder (TOC, TOR) to quantify biases toward object size and mention order. The findings reveal a robust image-side bias toward larger objects and a text-side bias toward the first-mentioned object, impacting image-caption matching and text-to-image generation. The work links these biases to CLIP's training dynamics and ViT architecture, underscoring the need for debiasing strategies to improve robustness in complex vision-language tasks.

Abstract

Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable performance in zero-shot classification tasks, yet their efficacy in handling complex multi-object scenarios remains challenging. This study presents a comprehensive analysis of CLIP's performance limitations in multi-object contexts through controlled experiments. We introduce two custom datasets, SimCO and CompCO, to evaluate CLIP's image and text encoders in various multi-object configurations. Our findings reveal significant biases in both encoders: the image encoder favors larger objects, while the text encoder prioritizes objects mentioned first in descriptions. We hypothesize these biases originate from CLIP's training process and provide evidence through analyses of the COCO dataset and CLIP's training progression. Additionally, we extend our investigation to Stable Diffusion models, revealing that biases in the CLIP text encoder significantly impact text-to-image generation tasks. Our experiments demonstrate how these biases affect CLIP's performance in image-caption matching and generation tasks, particularly when manipulating object sizes and their order in captions. This work contributes valuable insights into CLIP's behavior in complex visual environments and highlights areas for improvement in future vision-language models.

Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study

TL;DR

This study analyzes CLIP's limitations in multi-object scenes using two controlled datasets, SimCO and ComCO. It separately evaluates the image encoder (IOC, IOR) and the text encoder (TOC, TOR) to quantify biases toward object size and mention order. The findings reveal a robust image-side bias toward larger objects and a text-side bias toward the first-mentioned object, impacting image-caption matching and text-to-image generation. The work links these biases to CLIP's training dynamics and ViT architecture, underscoring the need for debiasing strategies to improve robustness in complex vision-language tasks.

Abstract

Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable performance in zero-shot classification tasks, yet their efficacy in handling complex multi-object scenarios remains challenging. This study presents a comprehensive analysis of CLIP's performance limitations in multi-object contexts through controlled experiments. We introduce two custom datasets, SimCO and CompCO, to evaluate CLIP's image and text encoders in various multi-object configurations. Our findings reveal significant biases in both encoders: the image encoder favors larger objects, while the text encoder prioritizes objects mentioned first in descriptions. We hypothesize these biases originate from CLIP's training process and provide evidence through analyses of the COCO dataset and CLIP's training progression. Additionally, we extend our investigation to Stable Diffusion models, revealing that biases in the CLIP text encoder significantly impact text-to-image generation tasks. Our experiments demonstrate how these biases affect CLIP's performance in image-caption matching and generation tasks, particularly when manipulating object sizes and their order in captions. This work contributes valuable insights into CLIP's behavior in complex visual environments and highlights areas for improvement in future vision-language models.

Paper Structure

This paper contains 33 sections, 7 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Dataset Design
  4. Experimental Setup
  5. Image Encoder Analysis
  6. Text Encoder Analysis
  7. Results and Analysis
  8. Text Encoder Analysis
  9. Text-based Object Classification (TOC)
  10. Text-based Object Retrieval (TOR)
  11. Image Encoder Analysis
  12. Image-based Object Classification (IOC)
  13. Image-based Object Retrieval (IOR)
  14. Bias Origin Hypothesis
  15. Image Encoder Bias
  16. ...and 18 more sections

Figures (7)

  • Figure 1: CLIP performance on multi-object image-caption matching. Left: Correct vs. incorrect captions with large object first. Right: Incorrect vs. reordered correct captions (large object last). Results show CLIP's bias for captions starting with larger objects, reducing accuracy when this order is altered.
  • Figure 2: Example images from the SimCO and CompCO datasets.
  • Figure 3: In the COCO dataset, the larger objects in an image are typically mentioned earlier in the captions
  • Figure 4: Evolution of TOR rate across training stages
  • Figure 5: Examples of SimCO dataset
  • ...and 2 more figures