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Found 19 papers, 11 papers with code
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications.
Boosting Few-Shot Learning via Attentive Feature Regularization
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor.
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method.
CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval
Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval.
Selective Volume Mixup for Video Action Recognition
In this paper, we propose a novel video augmentation strategy named Selective Volume Mixup (SV-Mix) to improve the generalization ability of deep models with limited training videos.
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Particularly, we use adversarial training to teach CgT-GAN to mimic the phrases of an external text corpus and CLIP-based reward to provide semantic guidance.
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Video prediction is a complex time-series forecasting task with great potential in many use cases.
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match.
3D Human Pose Estimation With Spatio-Temporal Criss-Cross Attention
On this basis, we devise STCFormer by stacking multiple STC blocks and further integrate a new Structure-enhanced Positional Embedding (SPE) into STCFormer to take the structure of human body into consideration.
Ranked #6 on
3D Human Pose Estimation
on MPI-INF-3DHP
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
By replacing a vanilla 2D attention with the LAPS, we could adapt a static transformer into a video one, with zero extra parameters and neglectable computation overhead ($\sim$2. 6\%).
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts.
Group Contextualization for Video Recognition
By utilizing calibrators to embed feature with four different kinds of contexts in parallel, the learnt representation is expected to be more resilient to diverse types of activities.
Ranked #3 on
Egocentric Activity Recognition
on EGTEA
Token Shift Transformer for Video Classification
It is worth noticing that our TokShift transformer is a pure convolutional-free video transformer pilot with computational efficiency for video understanding.
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways.
Motion Prediction Using Trajectory Cues
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.
Cross-sentence Pre-trained Model for Interactive QA matching
In addition to the context information captured at each word position, we incorporate a new quantity of context information jump to facilitate the attention weight formulation.
