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Found 12 papers, 8 papers with code
POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs
We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation.
Bridging the Divide: Reconsidering Softmax and Linear Attention
Widely adopted in modern Vision Transformer designs, Softmax attention can effectively capture long-range visual information; however, it incurs excessive computational cost when dealing with high-resolution inputs.
Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators
In response to this observation, we present a novel diffusion transformer framework incorporating an additional set of mediator tokens to engage with queries and keys separately.
Demystify Mamba in Vision: A Linear Attention Perspective
By exploring the similarities and disparities between the effective Mamba and subpar linear attention Transformer, we provide comprehensive analyses to demystify the key factors behind Mamba's success.
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
Nevertheless, the frozen visual encoder in autoregressive VLMs imposes constraints on the learning of conventional image triggers.
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.
Ranked #1 on
Generalized Referring Expression Segmentation
on gRefCOCO
(using extra training data)
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.
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization
Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios.
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.
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.
Contrastive Language-Image Pre-Training with Knowledge Graphs
Recent years have witnessed the fast development of large-scale pre-training frameworks that can extract multi-modal representations in a unified form and achieve promising performances when transferred to downstream tasks.
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module.
