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Found 13 papers, 4 papers with code
READ: Reciprocal Attention Discriminator for Image-to-Video Re-Identification
Person re-identification (re-ID) is the problem of visually identifying a person given a database of identities.
Image-To-Video Person Re-Identification
Video-Based Person Re-Identification
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
To address this issue, we introduce EXAONEPath, a novel foundational model trained on patches that have undergone stain normalization.
Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition
Vision and language models (VLMs) such as CLIP have showcased remarkable zero-shot recognition abilities yet face challenges in visio-linguistic compositionality, particularly in linguistic comprehension and fine-grained image-text alignment.
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web.
Expediting Contrastive Language-Image Pretraining via Self-distilled Encoders
Based on the unified text embedding space, ECLIPSE compensates for the additional computational cost of the momentum image encoder by expediting the online image encoder.
Masked Autoencoder for Unsupervised Video Summarization
Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary.
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web.
Exploring Temporally Dynamic Data Augmentation for Video Recognition
The magnitude of augmentation operations on each frame is changed by an effective mechanism, Fourier Sampling that parameterizes diverse, smooth, and realistic temporal variations.
Frequency Selective Augmentation for Video Representation Learning
FreqAug stochastically removes specific frequency components from the video so that learned representation captures essential features more from the remaining information for various downstream tasks.
VideoMix: Rethinking Data Augmentation for Video Classification
Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers.
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition
We present that selective regularization on this locally smoothed feature makes a model handle the low-frequency and high-frequency component distinctively, resulting in performance improvement.
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns.
Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
