Scene Graph Embeddings Using Relative Similarity Supervision
Authors: Paridhi Maheshwari, Ritwick Chaudhry, Vishwa Vinay2328-2336
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that this Ranking loss, coupled with an intuitive triple sampling strategy, leads to robust representations that outperform well-known contrastive losses on the retrieval task. The results are tabulated in Table 2 and we make the following observations: (a) Comparing across the 3 objective functions, it is evident that the proposed Ranking loss consistently outperforms the Triplet and Info NCE alternatives for any sampling. |
| Researcher Affiliation | Collaboration | Paridhi Maheshwari1*, Ritwick Chaudhry2* , Vishwa Vinay1 1 Adobe Research 2 Carnegie Mellon University parimahe@adobe.com, rchaudhr@andrew.cmu.edu, vinay@adobe.com |
| Pseudocode | No | The paper describes the model operations mathematically but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Dataset: We work on the Visual Genome (Krishna et al. 2017) dataset which is a collection of 108, 077 images and their scene graphs. |
| Dataset Splits | Yes | We divide the data into train, validation and test sets with a 70 : 20 : 10 split. |
| Hardware Specification | Yes | Training is performed on a Ubuntu 16.01 machine, using a single Tesla V100 GPU and Py Torch framework. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not provide specific version numbers for software dependencies or libraries used in the experiment. |
| Experiment Setup | Yes | The model consists of 5 GCN layers and is trained using Adam optimizer (Kingma and Ba 2014) for 100 epochs with learning rate 10 4 and batch size 16. The temperature parameter in Info NCE and Ranking loss has been set as λ = 1 and ν = 1 and the margin in Triplet loss as m = 0.5. For all multilayer perceptrons, we use Re LU activation and batch normalization (Ioffe and Szegedy 2015). |