Representation-Level Counterfactual Calibration for Debiased Zero-Shot Recognition

TL;DR

This paper introduces a causal inference-based method to improve zero-shot recognition by synthesizing counterfactual embeddings, effectively reducing context-based biases without retraining, and achieving state-of-the-art results.

Contribution

It presents a novel representation-level counterfactual calibration technique that enhances zero-shot model reliability by mitigating object-context shortcuts without retraining.

Findings

Significant accuracy improvements on context-sensitive benchmarks.

Effective reduction of hallucinated scores in zero-shot recognition.

Establishment of a new state-of-the-art in zero-shot performance.

Abstract

Object-context shortcuts remain a persistent challenge in vision-language models, undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences. We recast this issue as a causal inference problem and ask: Would the prediction remain if the object appeared in a different environment? To answer this at inference time, we estimate object and background expectations within CLIP's representation space, and synthesize counterfactual embeddings by recombining object features with diverse alternative contexts sampled from external datasets, batch neighbors, or text-derived descriptions. By estimating the Total Direct Effect and simulating intervention, we further subtract background-only activation, preserving beneficial object-context interactions while mitigating hallucinated scores. Without retraining or prompt design, our method substantially improves both worst-group and average accuracy on context-sensitive benchmarks, establishing a new zero-shot state of the art. Beyond performance, our framework provides a lightweight representation-level counterfactual approach, offering a practical causal avenue for debiased and reliable multimodal reasoning.