5 accepted papers
The goal of the image change captioning task is to capture the differences between two similar images and describe them in natural language. In this paper, we decompose this task into two sub-problems, i.e., fine-grained change feature learning and discrimination of changed regions. Compared with ex
Knowledge Distillation (KD) has attracted considerable attention as a typical model compression and knowledge transfer paradigm. However, most KD approaches are predicated on the implicit assumption: the deployed student model will exclusively encounter classes that have been seen during training, w…
Large language models (LLMs) increasingly rely on lengthy prompts to achieve complex tasks. However, longer prompts not only increase inference costs but pose significant challenges for limited context windows of LLMs. Prompt compression aims to accelerate inference in long-text scenarios while pres…
3D vision-language (3D-VL) reasoning, connecting natural language with 3D physical world, represents a milestone in advancing spatial intelligence. While transformer-based methods dominate 3D-VL research, their quadratic complexity and simplistic positional embedding mechanisms severely limits effec…
Open-Set Recognition (OSR) aims to accurately identify known classes while effectively rejecting unknown classes to guarantee reliability. Most existing OSR methods focus on learning in the spatial domain, where subtle texture and global structure are potentially intertwined. Empirical studies have…