5 accepted papers
Long-video understanding remains challenging due to extreme temporal redundancy, sparse yet decisive events, and the instability of long-horizon reasoning in visual-language models (VLMs). Existing agent-based methods invoke external micro-tools but remain static, repeatedly rebuilding long chains o
With the rapid development and adoption of 3D Gaussian Splatting (3DGS), the need for effective copyright protection has become increasingly critical. Existing watermarking techniques for 3DGS mainly focus on protecting rendered images via pre-trained decoders, leaving the underlying 3D Gaussian pri…
Similar to Language or Image LLMs, VideoLLMs are also plagued by hallucination issues. Hallucinations in videos not only manifest in the spatial dimension regarding the perception of the existence of visual objects (static) but also the temporal dimension influencing the perception of actions and ev…
Recent advancements in Large Vision-Language Models (LVLMs) highlight their ability to integrate and process multi-modal information. However, hallucinations—where generated content is inconsistent with input vision and instructions—remain a challenge. In this paper, we analyze LVLMs' layer-wise dec…
Recent advancements in language models pre-trained on large-scale corpora have significantly propelled developments in the NLP domain and advanced progress in multimodal tasks. In this paper we propose a Parameter-Efficient multimodal language model learning strategy named QaP (Querying as Prompt).…