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Found 27 papers, 8 papers with code
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications.
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
In this paper, we start with a conceptual definition of AGI and briefly review how NLP solves a wide range of tasks via a chat system.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
LION: Implicit Vision Prompt Tuning
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs.
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos.
Open-Set Fine-Grained Retrieval via Prompting Vision-Language Evaluator
PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios.
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization
And as a result, the dual-branch complementarity is effectively fused to promote a strong alliance.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion.
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.
Prompt-Matched Semantic Segmentation
Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks.
Fine-grained Retrieval Prompt Tuning
In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompting and feature adaptation.
Pro-tuning: Unified Prompt Tuning for Vision Tasks
To this end, we propose parameter-efficient Prompt tuning (Pro-tuning) to adapt frozen vision models to various downstream vision tasks.
HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval
It also achieves about +4. 9 AR on COCO and +3. 8 AR on Flickr30K than LightingDot and achieves comparable performance with the state-of-the-art (SOTA) fusion-based model METER.
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization
Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation.
Deep Encryption: Protecting Pre-Trained Neural Networks with Confusion Neurons
Having consumed huge amounts of training data and computational resource, large-scale pre-trained models are often considered key assets of AI service providers.
Differentiable Convolution Search for Point Cloud Processing
It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration
In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
Spatio-Temporal Graph Structure Learning for Traffic Forecasting
To address these issues, we propose a novel framework named Structure Learning Convolution (SLC) that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting.
Ranked #3 on
Traffic Prediction
on METR-LA
Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification
Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images.
DATA: Differentiable ArchiTecture Approximation
Neural architecture search (NAS) is inherently subject to the gap of architectures during searching and validating.
Differentiable Architecture Search with Ensemble Gumbel-Softmax
For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency.
Deep Discriminative Clustering Analysis
Traditional clustering methods often perform clustering with low-level indiscriminative representations and ignore relationships between patterns, resulting in slight achievements in the era of deep learning.
Structure-Aware Convolutional Neural Networks
Convolutional neural networks (CNNs) are inherently subject to invariable filters that can only aggregate local inputs with the same topological structures.
Deep Adaptive Image Clustering
The main challenge is that the ground-truth similarities are unknown in image clustering.
Ranked #8 on
Image Clustering
on Tiny-ImageNet
