# UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces

arXiv: https://arxiv.org/abs/2312.15715 · HTML: https://mlsanity.com/paper/2312.15715/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 110 of its 132 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 30 of 110)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)

## Built on by (most cited first, 22 of 22)

- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding](https://mlsanity.com/paper/2503.06287/) (2025)
- [EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model](https://mlsanity.com/paper/2406.20076/) (2024)
- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [UniVS: Unified and Universal Video Segmentation with Prompts as Queries](https://mlsanity.com/paper/2402.18115/) (2024)
- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing](https://mlsanity.com/paper/2410.10167/) (2024)
- [ActionVOS: Actions as Prompts for Video Object Segmentation](https://mlsanity.com/paper/2407.07402/) (2024)
- [HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models](https://mlsanity.com/paper/2409.10419/) (2024)
- [R2-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations](https://mlsanity.com/paper/2403.04924/) (2024)
- [Deep Instruction Tuning for Segment Anything Model](https://mlsanity.com/paper/2404.00650/) (2024)
- [RESAnything: Attribute Prompting for Arbitrary Referring Segmentation](https://mlsanity.com/paper/2505.02867/) (2025)
- [VoCap: Video Object Captioning and Segmentation from Any Prompt](https://mlsanity.com/paper/2508.21809/) (2025)
- [GroPrompt: Efficient Grounded Prompting and Adaptation for Referring Video Object Segmentation](https://mlsanity.com/paper/2406.12834/) (2024)
- [Autoregressive Universal Video Segmentation Model](https://mlsanity.com/paper/2508.19242/) (2025)
- [Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention](https://mlsanity.com/paper/2512.24323/) (2025)
- [X2SAM: Any Segmentation in Images and Videos](https://mlsanity.com/paper/2605.00891/) (2026)
- [Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches](https://mlsanity.com/paper/2503.12191/) (2025)
- [Mitigating Query Selection Bias in Referring Video Object Segmentation](https://mlsanity.com/paper/2509.13722/) (2025)
- [Temporal Prompting Matters: Rethinking Referring Video Object Segmentation](https://mlsanity.com/paper/2510.07319/) (2025)
- [LTCA: Long-range Temporal Context Attention for Referring Video Object Segmentation](https://mlsanity.com/paper/2510.08305/) (2025)
- [Unlocking Motion in Expressions: Temporal Calibration for Referring Video Object Segmentation](https://mlsanity.com/paper/2608.16332/) (2026)

## Abstract (arXiv)

The reference-based object segmentation tasks, namely referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video object segmentation (VOS), aim to segment a specific object by utilizing either language or annotated masks as references. Despite significant progress in each respective field, current methods are task-specifically designed and developed in different directions, which hinders the activation of multi-task capabilities for these tasks. In this work, we end the current fragmented situation and propose UniRef++ to unify the four reference-based object segmentation tasks with a single architecture. At the heart of our approach is the proposed UniFusion module which performs multiway-fusion for handling different tasks with respect to their specified references. And a unified Transformer architecture is then adopted for achieving instance-level segmentation. With the unified designs, UniRef++ can be jointly trained on a broad range of benchmarks and can flexibly complete multiple tasks at run-time by specifying the corresponding references. We evaluate our unified models on various benchmarks. Extensive experimental results indicate that our proposed UniRef++ achieves state-of-the-art performance on RIS and RVOS, and performs competitively on FSS and VOS with a parameter-shared network. Moreover, we showcase that the proposed UniFusion module could be easily incorporated into the current advanced foundation model SAM and obtain satisfactory results with parameter-efficient finetuning. Codes and models are available at urlhttps://github.com/FoundationVision/UniRef.