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..
Collective Deep Quantization for Efficient Cross-Modal Retrieval
Authors: Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that CDQ yields state of the art cross-modal retrieval results on standard benchmarks. |
| Researcher Affiliation | Academia | Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu KLiss, MOE; TNList; School of Software, Tsinghua University, Beijing, China EMAIL EMAIL |
| Pseudocode | No | The paper describes algorithms but does not include a figure, block, or section labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its open-source code. |
| Open Datasets | Yes | NUS-WIDE (Chua et al. 2009) is a public web image dataset. MIRFlickr (Huiskes and Lew 2008) consists of 25,000 images collected from the Flickr website. |
| Dataset Splits | Yes | In NUS-WIDE, we randomly select 100 pairs per class as the query set, 500 pairs per class as the training set and 50 pairs per class as the validation set. In MIR-Flickr, we randomly select 1000 pairs as the query set, 4000 pairs as the training set and 1000 pairs as the validation set. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We use mini-batch SGD with 0.9 momentum, fix mini-batch size as 64, and cross-validate the learning rate. We follow similar strategy in (Long et al. 2016): (1) set the dimension of bottleneck layer D = 128 such that the composite quantizer can quantize the bottleneck representations accurately; (2) set K = 256 codewords for each codebook; (3) for each data point, the binary code of all M subspaces requires B = M log2 K = 8M bits (i.e. M bytes) for compact coding, where we set M = B/8 as B is known. |