Search Results for author:
Found 18 papers, 12 papers with code
Agent Attention: On the Integration of Softmax and Linear Attention
Specifically, the Agent Attention, denoted as a quadruple $(Q, A, K, V)$, introduces an additional set of agent tokens $A$ into the conventional attention module.
FLatten Transformer: Vision Transformer using Focused Linear Attention
The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks.
Resolution Adaptive Networks for Efficient Inference
Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks.
Adaptive Focus for Efficient Video Recognition
In this paper, we explore the spatial redundancy in video recognition with the aim to improve the computational efficiency.
Adaptive Rotated Convolution for Rotated Object Detection
In our ARC module, the convolution kernels rotate adaptively to extract object features with varying orientations in different images, and an efficient conditional computation mechanism is introduced to accommodate the large orientation variations of objects within an image.
Ranked #3 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Dynamic Perceiver for Efficient Visual Recognition
Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.
Latency-aware Spatial-wise Dynamic Networks
The latency prediction model can efficiently estimate the inference latency of dynamic networks by simultaneously considering algorithms, scheduling strategies, and hardware properties.
Latency-aware Unified Dynamic Networks for Efficient Image Recognition
Dynamic computation has emerged as a promising avenue to enhance the inference efficiency of deep networks.
Learning to Weight Samples for Dynamic Early-exiting Networks
Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers.
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation
Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency.
Fine-grained Recognition with Learnable Semantic Data Augmentation
Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories.
SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning
Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched.
Dynamic Neural Networks: A Survey
Dynamic neural network is an emerging research topic in deep learning.
CAM-loss: Towards Learning Spatially Discriminative Feature Representations
The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification.
Computation-efficient Deep Learning for Computer Vision: A Survey
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks.
GSVA: Generalized Segmentation via Multimodal Large Language Models
Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the image.
Generalized Referring Expression Segmentation
Referring Expression
+1
Mask Grounding for Referring Image Segmentation
To tackle this challenge, we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features, by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects.
Ranked #2 on
Referring Expression Segmentation
on RefCOCO testB
GRA: Detecting Oriented Objects through Group-wise Rotating and Attention
GRA can adaptively capture fine-grained features of objects with diverse orientations, comprising two key components: Group-wise Rotating and Group-wise Attention.
