Blind text-only likelihood models match or exceed CLIP on many compositionality benchmarks because positives and negatives differ systematically in length, plausibility, or image style.
Blindfold Baselines for Embodied QA
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abstract
We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question by intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.
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A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks
Blind text-only likelihood models match or exceed CLIP on many compositionality benchmarks because positives and negatives differ systematically in length, plausibility, or image style.