| _ | say | _ |
|---|
| Bad teacher or unruly student: Can deep learning | say | something in Image Forensics analysis? |
| Benford's law: What does it | say | on adversarial images? |
| Can cargo drones solve air freight's logjams? A drone startup | say | s its big vertical-takeoff flier would be quick to land, load, and take off again |
| Categorizing plant images at the variety level: Did you | say | fine-grained? |
| CLIPN for Zero-Shot OOD Detection: Teaching CLIP to | say | No |
| Connecting What to | say | With Where to Look by Modeling Human Attention Traces |
| Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers | say | No |
| Is There Anything New to | say | About SIFT Matching? |
| Know More | say | Less: Image Captioning Based on Scene Graphs |
| say | As You Wish: Fine-Grained Control of Image Caption Generation With Abstract Scene Graphs |
| say | CHEESE: Common Human Emotional Expression Set Encoder and Its Application to Analyze Deceptive Communication |
| say | it to see it: A speech based immersive model retrieval system |
| say | No to Redundant Information: Unsupervised Redundant Feature Elimination for Active Learning |
| say | Yes to the Dress: Shape and Style Transfer Using Conditional GANs |
| Spoken Attributes: Mixing Binary and Relative Attributes to | say | the Right Thing |
| What Do Datasets | say | About Saliency Models? |
| What does computer vision | say | about face reading? |
| What Men | say | , What Women Hear: Finding Gender-Specific Meaning Shades |
| What You | say | Is Not What You Do: Studying Visio-Linguistic Models for TV Series Summarization |
| What You | say | Is What You See: Interactive Generation, Manipulation and Modification of 3-D Shapes Based on Verbal Descriptions |
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