TL;DR
VLTok is a novel 1D hybrid tokenizer that unifies V isual and L anguage representations in a shared Tok en space through a self-prompted training paradigm, and achieves state-of-the-art performance in both image reconstruction and image generation.
VLZip is introduced, a framework that unifies visual and textual compression for high-fidelity reasoning within a pure Transformer, and establishes an efficient and powerful new standard for long-context multimodal AI.
SPIRAL (Self-improving Path Integration and Realignment), a self-supervised alignment framework that closes this gap using only the model's own text-path behavior as supervision, requiring no external teachers or additional annotations, generalize to out-of-domain benchmarks, confirming that effective VTC hinges on ali...
UniProbe is introduced, a lightweight, unified, learnable detector that models a frozen LVLM's heterogeneous computational trace from a single forward pass and achieves state-of-the-art token-level and object-hallucination detection.
Slot2Text, a dual-mode surgical MLLM that replaces dense representations of visual input with a compact set of regions encoded as slot latents, establishes compact slot latents as an efficient default visual interface for surgical MLLMs, with grounded reasoning invoked when greater spatial traceability is required.
Vision-language models (VLMs), such as contrastive language-image pre-training (CLIP), exhibit powerful zero-shot generalization capabilities. Parameter-efficient fine-tuning (PEFT) techniques, notably prompt learning, have been extensively explored to adapt these models to downstream tasks. However, their efficacy rem...
Inspired by the linguistic phenomenon of code-switching, MMCS interleaves vision and language by replacing textual entities with their corresponding visual objects, enforcing local vision-language grounding.
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