2022-11-21 更新
Prompt-based Learning for Unpaired Image Captioning
Authors:Peipei Zhu, Xiao Wang, Lin Zhu, Zhenglong Sun, Weishi Zheng, Yaowei Wang, Changwen Chen
Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Existing works usually tackle this task using adversarial learning and visual concept reward based on reinforcement learning. However, these existing works were only able to learn limited cross-domain information in vision and language domains, which restrains the captioning performance of UIC. Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task. This research is also motivated by recent successes of prompt learning in many downstream multi-modal tasks, including image-text retrieval and vision question answering. In this work, a semantic prompt is introduced and aggregated with visual features for more accurate caption prediction under the adversarial learning framework. In addition, a metric prompt is designed to select high-quality pseudo image-caption samples obtained from the basic captioning model and refine the model in an iterative manner. Extensive experiments on the COCO and Flickr30K datasets validate the promising captioning ability of the proposed model. We expect that the proposed prompt-based UIC model will stimulate a new line of research for the VL-PTMs based captioning.
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Data-Centric Debugging: mitigating model failures via targeted data collection
Authors:Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri, Soheil Feiz
Deep neural networks can be unreliable in the real world when the training set does not adequately cover all the settings where they are deployed. Focusing on image classification, we consider the setting where we have an error distribution $\mathcal{E}$ representing a deployment scenario where the model fails. We have access to a small set of samples $\mathcal{E}{sample}$ from $\mathcal{E}$ and it can be expensive to obtain additional samples. In the traditional model development framework, mitigating failures of the model in $\mathcal{E}$ can be challenging and is often done in an ad hoc manner. In this paper, we propose a general methodology for model debugging that can systemically improve model performance on $\mathcal{E}$ while maintaining its performance on the original test set. Our key assumption is that we have access to a large pool of weakly (noisily) labeled data $\mathcal{F}$. However, naively adding $\mathcal{F}$ to the training would hurt model performance due to the large extent of label noise. Our Data-Centric Debugging (DCD) framework carefully creates a debug-train set by selecting images from $\mathcal{F}$ that are perceptually similar to the images in $\mathcal{E}{sample}$. To do this, we use the $\ell_2$ distance in the feature space (penultimate layer activations) of various models including ResNet, Robust ResNet and DINO where we observe DINO ViTs are significantly better at discovering similar images compared to Resnets. Compared to LPIPS, we find that our method reduces compute and storage requirements by 99.58\%. Compared to the baselines that maintain model performance on the test set, we achieve significantly (+9.45\%) improved results on the debug-heldout sets.
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