Neural Baby Talk

We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different. Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalkComment: 12 pages, 7 figures, CVPR 201

Effect of cadmium on the defense response of Pacific oyster Crassostrea gigas to Listonella anguillarum challenge

Heavy metal pollution can affect the immune capability of organisms. We evaluated the effect of cadmium (Cd) on the defense responses of the Pacific oyster Crassostrea gigas to Listonella anguillarum challenge. The activities of several important defensive enzymes, including superoxide dismutase (SOD), glutathione peroxidase (GPx), acid phosphatase (ACP), Na+, K+ -ATPase in gills and hepatopancreas, and phenoloxidase-like (POL) enzyme in hemolymph were assayed. In addition, the expression levels of several genes, including heat shock protein 90 (HSP90), metallothionein (MT), and bactericidal/permeability increasing (BPI) protein were quantified by fluorescent quantitative PCR. The enzyme activities of SOD, ACP, POL, and GPx in hepatopancreas, and the expression of HSP90 were down-regulated, whereas GPx activity in the gill, Na+, K+-ATPase activities in both tissues, and MT expression was increased in Cdexposed oysters post L. anguillarum challenge. However, BPI expression was not significantly altered by co-stress of L. anguillarum infection and cadmium exposure. Our results suggest that cadmium exposure alters the oysters' immune responses and energy metabolism following vibrio infection.Heavy metal pollution can affect the immune capability of organisms. We evaluated the effect of cadmium (Cd) on the defense responses of the Pacific oyster Crassostrea gigas to Listonella anguillarum challenge. The activities of several important defensive enzymes, including superoxide dismutase (SOD), glutathione peroxidase (GPx), acid phosphatase (ACP), Na+, K+ -ATPase in gills and hepatopancreas, and phenoloxidase-like (POL) enzyme in hemolymph were assayed. In addition, the expression levels of several genes, including heat shock protein 90 (HSP90), metallothionein (MT), and bactericidal/permeability increasing (BPI) protein were quantified by fluorescent quantitative PCR. The enzyme activities of SOD, ACP, POL, and GPx in hepatopancreas, and the expression of HSP90 were down-regulated, whereas GPx activity in the gill, Na+, K+-ATPase activities in both tissues, and MT expression was increased in Cdexposed oysters post L. anguillarum challenge. However, BPI expression was not significantly altered by co-stress of L. anguillarum infection and cadmium exposure. Our results suggest that cadmium exposure alters the oysters' immune responses and energy metabolism following vibrio infection

Multi-Modal Answer Validation for Knowledge-Based VQA

The problem of knowledge-based visual question answering involves answering questions that require external knowledge in addition to the content of the image. Such knowledge typically comes in various forms, including visual, textual, and commonsense knowledge. Using more knowledge sources increases the chance of retrieving more irrelevant or noisy facts, making it challenging to comprehend the facts and find the answer. To address this challenge, we propose Multi-modal Answer Validation using External knowledge (MAVEx), where the idea is to validate a set of promising answer candidates based on answer-specific knowledge retrieval. Instead of searching for the answer in a vast collection of often irrelevant facts as most existing approaches do, MAVEx aims to learn how to extract relevant knowledge from noisy sources, which knowledge source to trust for each answer candidate, and how to validate the candidate using that source. Our multi-modal setting is the first to leverage external visual knowledge (images searched using Google), in addition to textual knowledge in the form of Wikipedia sentences and ConceptNet concepts. Our experiments with OK-VQA, a challenging knowledge-based VQA dataset, demonstrate that MAVEx achieves new state-of-the-art results. Our code is available at https://github.com/jialinwu17/MAVEXComment: AAAI 202