Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
Authors: Zhuoyan Luo, Yicheng Xiao, Yong Liu, Shuyan Li, Yitong Wang, Yansong Tang, Xiu Li, Yujiu Yang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on popular RVOS benchmarks, and our method outperforms state-of-the-art competitors on all benchmarks by a remarkable margin. |
| Researcher Affiliation | Collaboration | 1Tsinghua Shenzhen International Graduate School, Tsinghua University 2Byte Dance Inc. 3Engineering Department, University of Cambridge |
| Pseudocode | No | The paper describes its approach using text and diagrams but does not include pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/Robert Luo1/Neur IPS2023_SOC. |
| Open Datasets | Yes | We evaluate our model on four prevalent RVOS benchmarks: Ref-You Tube-VOS [36], Ref-DAVIS17 [16], A2D-Sentences, and JHMDB-Sentences [8]. |
| Dataset Splits | Yes | We evaluate our model on four prevalent RVOS benchmarks: Ref-You Tube-VOS [36], Ref-DAVIS17 [16], A2D-Sentences, and JHMDB-Sentences [8]. Following [1, 42], we measure the effectiveness of our model by criteria of Precision@K, Overall Io U, Mean Io U and MAP over 0.50:0.05:0.95 for A2D-Sentences and JHMDB-Sentences. Meanwhile, we adopt standard evaluation metrics: region similarity(J ), contour accuracy (F) and their average value (J &F) on Ref-You Tube-VOS and Ref-DAVIS17. |
| Hardware Specification | Yes | The models are trained with eight 32GB V100 GPUs in default. Specifically, our SOC runs at 32.3 FPS on single 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'Video Swin Transformer [27] and Ro BERTa [23] as our encoder' but does not specify software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | The number of frame-level queries Of and video-level queries Ov are set as 20 in default. We feed the model windows of w = 8 frames during training. The coefficients for losses are set as λcls = 2, λL1 = 2, λgiou = 2, λdice = 2, λfocal = 5, λcon = 1. |