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Found 12 papers, 4 papers with code
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge.
Self-Sufficient Framework for Continuous Sign Language Recognition
The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition.
Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition
Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are filmed in studios with a static monochromatic background.
Generative Bias for Robust Visual Question Answering
In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB.
Investigating Top-$k$ White-Box and Transferable Black-box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
Investigating Top-k White-Box and Transferable Black-Box Attack
It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength.
Single-Modal Entropy based Active Learning for Visual Question Answering
In this work, we address Active Learning in the multi-modal setting of Visual Question Answering (VQA).
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions.
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2. 2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)".
MCDAL: Maximum Classifier Discrepancy for Active Learning
Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition; however, GAN is usually known to suffer from instability and sensitivity to hyper-parameters.
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation
In this work, we address the issues of missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand.
Optical Flow Estimation from a Single Motion-blurred Image
A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner.
