A recent CVPR 2026 highlight paper introduces DeBias-CLIP, a model that fixes the “early-token bias” in vision-language models like CLIP, which struggle to align complex scenes with dense, paragraph-length descriptions because their training is dominated by short captions. The research identifies that both human- and AI-generated long captions often begin with a one-sentence summary, and existing models latch onto this opening to achieve training objectives, ignoring detailed descriptions. Testing this limitation by swapping the first and second sentences (“Move”) severely degrades previous models’ abilities to find the matching image, while removing the summary sentence (“Remove”) causes an even more dramatic drop. DeBias-CLIP, however, stays consistently accurate in these scenarios. The model is a drop-in replacement requiring no additional trainable parameters, using three caption-level augmentations: “Remove” drops the opening summary sentence; “Sample” randomly selects a subset of remaining sentences; and “Pad” adds blank “padding” tokens to the beginning of the text sequence. This forces the model to align images with dense, highly detailed text rather than simple concepts.