Publicación: HALLUCINATING SALIENCY MAPS FOR FINE-GRAINED IMAGE CLASSIFICATION FOR LIMITED DATA DOMAINS
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2021
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IN PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS
Resumen
MOST OF THE SALIENCY METHODS ARE EVALUATED ON THEIRABILITY TO GENERATE SALIENCY MAPS, AND NOT ON THEIR FUNCTIONALITY INA COMPLETE VISION PIPELINE, LIKE FOR INSTANCE, IMAGE CLASSI?CATION.IN THE CURRENT PAPER, WE PROPOSE AN APPROACH WHICH DOES NOTREQUIRE EXPLICIT SALIENCY MAPS TO IMPROVE IMAGE CLASSI?CATION,BUT THEY ARE LEARNED IMPLICITELY, DURING THE TRAINING OF AN END-TO-END IMAGE CLASSI?CATION TASK. WE SHOW THAT OUR APPROACHOBTAINS SIMILAR RESULTS AS THE CASE WHEN THE SALIENCY MAPSARE PROVIDED EXPLICITELY. COMBINING RGB DATA WITH SALIENCYMAPS REPRESENTS A SIGNI?CANT ADVANTAGE FOR OBJECT RECOGNITION,ESPECIALLY FOR THE CASE WHEN TRAINING DATA IS LIMITED. WE VALIDATEOUR METHOD ON SEVERAL DATASETS FOR ?NE-GRAINED CLASSI?CATIONTASKS (FLOWERS, BIRDS AND CARS). IN ADDITION, WE SHOW THAT OURSALIENCY ESTIMATION METHOD, WHICH IS TRAINED WITHOUT ANY SALIENCYGROUNDTRUTH DATA, OBTAINS COMPETITIVE RESULTS ON REAL IMAGESALIENCY BENCHMARK (TORONTO), AND OUTPERFORMS DEEP SALIENCYMODELS WITH SYNTHETIC IMAGES (SID4VAM).
