[PDF] Top 20 Comparison of Image Captioning Methods

... Image captioning is a process of generating image descriptions for a detailed understanding of the various elements of the ...the image , the background or the setting of the environment in ... See full document

... existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation ...the image ... See full document

... Model Performance We report our results with fre- quently used evaluation metrics: BLEU-1,2,3,4 (Papineni et al. 2002), METEOR (Banerjee and Lavie 2005), CIDEr (Vedantam, Zitnick, and Parikh 2015) and SPICE (Anderson et ... See full document

... state-of-the-art methods For offline evaluation on MSCOCO dataset, we compare our model with the current state-of-the-art methods: Adaptive (Lu et ...detect image regions, and a top-down mechanism to ... See full document

... on image retrieval and the assumption that similar images have sim- ilar diagnoses; we show that it is a strong base- line outperforming the state of the art in at least one ...biomedical image processing ... See full document

... Two recent approaches have achieved state-of-the-art results in image caption- ing. The first uses a pipelined process where a set of candidate words is gen- erated by a convolutional neural network (CNN) trained ... See full document

... the image. In particular, methods that incorporate bounding box attention (as opposed to relying on coarse feature maps), consistently have lower hallucination as measured by our CHAIR ...standard ... See full document

... in image captioning (Rennie et al. 2017). Policy- gradient methods for reinforcement learning are proved to be suitable for training captioning ...the captioning model using MLE, and ... See full document

... selecting parameter settings resulting in the low- est validation set perplexity, unless specified other- wise. Settings we take as fixed include a minimum vocabulary threshold of 5, weight optimization us- ing RMSprop ... See full document

... input image. Using three image captioning benchmark datasets, we show that our approach provides more ac- curate results compared to the state-of-the- art data-driven methods in terms of both ... See full document

... learning methods [18, 69], we propose a strategy which exploits the large pool of unstructured raw-comments from AVA and discovers latent structures corresponding to meaningful photographic concepts using Latent ... See full document

... different methods for aggregating multiple instances of the same category, in addition to choosing the biggest in- stance and the instance closest to the image cen- ...the image centre (max pooling), ... See full document

... as image captioning, is a long stand- ing problem in computer vision and computational ...proposed methods based on deep neural networks have demonstrated convinc- ing results in this task, (Xu et ... See full document

... for image caption gener- ation. Data-driven matching methods have shown to be effective for a variety of com- plex problems in Computer ...These methods reduce an inference problem for an unknown ... See full document

... neural image captioning model that uses attention, and fix the attention to pre- determined areas in the ...effective methods to control the attention and find that these are producing expected re- ... See full document

... this image captioning framework and so that the attributes can be introduced to the CNN and RNN ...feeding image representations in various ways to explore mutual and fuzzy relationship between ...of ... See full document

... automatic image captioning (Farhadi et ...ages. Captioning systems trained on single images struggle to be pragmatic in this sense, producing either very general or hyper-specific ... See full document

... Recently, the most popular generative model–generative adversarial nets (GANs) (Goodfellow et al. 2014)–has achieved great success in computer vision tasks. But un- like the deterministic continuous mapping from random ... See full document

... The comparison between different layers (C4, C3, FC2) confirms the effectiveness of jointly us- ing multi-level features. The fused features by the top-down multi-scale encoder (i.e. TopDown-) prove the most ... See full document

... available image-caption training data is limited, many image collections are augmented with ground-truth text fragments such as semantic attributes ...(i.e., image tags) or object ...specific) ... See full document