Page: 1 2 3 4 5 6 7 8... 870
61 |
Word separation in continuous sign language using isolated signs and post-processing ...
|
|
|
|
BASE
|
|
Show details
|
|
62 |
Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language ...
|
|
|
|
BASE
|
|
Show details
|
|
63 |
ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language ...
|
|
|
|
BASE
|
|
Show details
|
|
64 |
TFS Recognition: Investigating MPH]{Thai Finger Spelling Recognition: Investigating MediaPipe Hands Potentials ...
|
|
|
|
BASE
|
|
Show details
|
|
65 |
Sign Language Video Retrieval with Free-Form Textual Queries ...
|
|
|
|
BASE
|
|
Show details
|
|
68 |
Sign Language Recognition System using TensorFlow Object Detection API ...
|
|
|
|
BASE
|
|
Show details
|
|
69 |
Τρισδιάστατη ανακατασκευή ανθρωπίνου σώματος, χεριών και προσώπου με εφαρμογές στην αναγνώριση νοηματικής γλώσσας ...
|
|
|
|
BASE
|
|
Show details
|
|
70 |
Biasing Like Human: A Cognitive Bias Framework for Scene Graph Generation ...
|
|
|
|
BASE
|
|
Show details
|
|
71 |
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
72 |
Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework ...
|
|
|
|
BASE
|
|
Show details
|
|
73 |
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition ...
|
|
|
|
BASE
|
|
Show details
|
|
74 |
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos ...
|
|
|
|
BASE
|
|
Show details
|
|
76 |
EnvEdit: Environment Editing for Vision-and-Language Navigation ...
|
|
|
|
BASE
|
|
Show details
|
|
77 |
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
78 |
Natural Language Descriptions of Deep Visual Features ...
|
|
|
|
Abstract:
Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons and behaviors in any network. Is a richer characterization of neuron-level computation possible? We introduce a procedure (called MILAN, for mutual-information-guided linguistic annotation of neurons) that automatically labels neurons with open-ended, compositional, natural language descriptions. Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active. MILAN produces fine-grained descriptions that capture categorical, relational, and logical structure in learned features. These descriptions obtain high agreement with ... : To be published as a conference paper at ICLR 2022 ...
|
|
Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://arxiv.org/abs/2201.11114 https://dx.doi.org/10.48550/arxiv.2201.11114
|
|
BASE
|
|
Hide details
|
|
80 |
IterVM: Iterative Vision Modeling Module for Scene Text Recognition ...
|
|
|
|
BASE
|
|
Show details
|
|
Page: 1 2 3 4 5 6 7 8... 870
|
|