3 accepted papers
Multimodal image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image that preserves *fine local details* while maintaining *globally consistent appearance*. Most existing approaches build shared representations on 2D feature grids, which exce…
Speculative decoding (SD) addresses the high inference costs of large language models (LLMs) by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft-verify methods use binary decisions: accept or fully recompute. Yet we find that many rejected t…
Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However this method often leads to the generation of irrelevant con…