arXiv:1811.00613
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
원문 초록
We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA, seeing an up to 29% absolute gain in performance over published baselines.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
From Roots to Rewards: Dynamic Tree Reasoning with RL
Describe Anything Model for Visual Question Answering on Text-rich Images
Warehouse Spatial Question Answering with LLM Agent
L0: Reinforcement Learning to Become General Agents
Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.