3 accepted papers
Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample. However, in most existing works, easy and hard samples are treated equally by each classifier during training, w…
Early exiting has demonstrated great potential in accelerating the inference of pre-trained language models (PLMs) by enabling easy samples to exit at shallow layers, eliminating the need for executing deeper layers. However, existing early exiting methods primarily rely on class-relevant logits to
Zero-shot Referring Expression Comprehension (REC) aims at locating an object described by a natural language query without training on task-specific datasets. Current approaches often utilize Vision-Language Models (VLMs) to perform region-text matching based on region proposals. However, this may…