2208.01626] Prompt-to-Prompt Image Editing with Cross Attention Control Skip to main content We gratefully

2406.16008] Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization Skip to main

2406.16008] Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization Skip to main

1706.03762] Attention Is All You Need Happy Open Access Week from arXiv! YOU make open access possible! Tell

2208.01626] Prompt-to-Prompt Image Editing with Cross Attention Control Skip to main content We gratefully

2109.01349] Dual-Camera Super-Resolution with Aligned Attention Modules Skip to main content We gratefully acknowledge support from

1706.03762] Attention Is All You Need Skip to main content We gratefully acknowledge support from the

2205.14135] FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Skip to main content We

2404.08634] When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models Skip to main

2307.13108] An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment Skip to

2109.01349] Dual-Camera Super-Resolution with Aligned Attention Modules Skip to main content We gratefully acknowledge support from

2410.13835] Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs Skip to main content

1412.7755] Multiple Object Recognition with Visual Attention Skip to main content We gratefully acknowledge support from the

2006.14615] LayoutTransformer: Layout Generation and Completion with Self-attention Skip to main content We gratefully acknowledge

2207.13298] Is Attention All That NeRF Needs? Skip to main content We gratefully acknowledge support from the

2307.08691] FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning Skip to main content We

2307.11353] What can a Single Attention Layer Learn? A Study Through the Random Features

a number of different attention models proposed. However, the scale at which attention needs to be

2306.07998] Contrastive Attention Networks for Attribution of Early Modern Print Skip to main

1409.0473] Neural Machine Translation by Jointly Learning to Align and Translate Skip

2107.14285] ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation Skip to

Query Models with Intention, by Marta Garnelo and 1 other authors View PDF Abstract: Attention-based models have been

can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video

the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion

the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion

6 other authors View PDF HTML (experimental) Abstract: We present personalized residuals and localized attention-guided sampling for

cameras effectively, we additionally propose to use Transformers with a cross-view attention mechanism that models spatial

on long physical interaction sequences with multiple interactions among different objects. We hypothesize that selective temporal attention during approximate mental simulations

on long physical interaction sequences with multiple interactions among different objects. We hypothesize that selective temporal attention during approximate mental simulations

of 320k sequences, and 100 million frames. 2) Architecture: We design an articulated attention mechanism ArtAttention that incorporates

Chai and 7 other authors View PDF HTML (experimental) Abstract: AI agents have drawn increasing attention mostly on their ability

LLMs and their integration with other data modalities, multi-modal 3D perception attracts more attention due to its

Mohammed Suhail and 3 other authors View PDF Abstract: Neural rendering has received tremendous attention since the advent

analyzed biases in vision and pre-trained language models individually - however, less attention has been paid to

Mohammed Suhail and 3 other authors View PDF Abstract: Neural rendering has received tremendous attention since the advent

the images of persons from the group photo into the attention modules and employ

of the model, such as the weight matrices or the attention patterns. In this

HTML (experimental) Abstract: Surprising videos, such as funny clips, creative performances, or visual illusions, attract significant attention. Enjoyment of these

3D scene understanding has primarily focused on short-term change tracking from dense observations, while little attention has been paid to

advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA

output of large language models into the individual contributions of attention heads and MLPs

and efficiency due to the computational demands of long-range attention mechanisms. To address

experimental) Abstract: We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for

of the model, such as the weight matrices or the attention patterns. In this

6 other authors View PDF HTML (experimental) Abstract: We present personalized residuals and localized attention-guided sampling for

and efficiency due to the computational demands of long-range attention mechanisms. To address

to synthesize pixels. Because keypoints can contain errors, we propose a cross-modal attention scheme to select

To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint

every step of the reverse process through a 3D-aware feature attention mechanism that correlates the

flow smoothness through a hybrid loss function, implemented via a lightweight temporal attention architecture. Applied to

domains not seen at training time. Additionally, we propose a novel keypoint position-guided attention mechanism which disentangles spatial

the style to a single view using a training-free semantic-attention mechanism in a

explicit image correspondence to direct editing. Specifically, the key components include an attention manipulation module and

distortions and scene geometry. To enhance distortion prediction, we incorporate edge-aware attention, focusing the model

frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top

PDF Abstract: The task of person re-identification has recently received rising attention due to the

image by attending over the clues automatically extracted from the guidebook. Supervising attention with country-level pseudo

the next actions while conforming to social behaviors by engaging to joint attention. Our key innovation is

To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint

the style to a single view using a training-free semantic-attention mechanism in a

every step of the reverse process through a 3D-aware feature attention mechanism that correlates the

domains not seen at training time. Additionally, we propose a novel keypoint position-guided attention mechanism which disentangles spatial

explicit image correspondence to direct editing. Specifically, the key components include an attention manipulation module and

PDF Abstract: The task of person re-identification has recently received rising attention due to the

frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top

distortions and scene geometry. To enhance distortion prediction, we incorporate edge-aware attention, focusing the model

drawing a red circle around an object, we can direct the model's attention to that

multi-view generation, we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view

point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the

can only be unhackable if one of them is constant. We thus turn our attention to deterministic

neglecting the non-expert user access to agents and paying little attention to application

and 10 other authors View PDF HTML (experimental) Abstract: Action understanding has attracted long-term attention. It can be formed

To further secure the style consistency across views, we introduce a cross-attention decoder to predict

to 3D points, thus yielding general and rich point representations. We additionally integrate an attention mechanism to aggregate

video-text triplets. Additionally, we propose several methods to enhance our model, including special attention designs, improved training strategies

in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the

Chen and 4 other authors View PDF Abstract: Multi-view image generation attracts particular attention these days due to

A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange

To further secure the style consistency across views, we introduce a cross-attention decoder to predict

to 3D points, thus yielding general and rich point representations. We additionally integrate an attention mechanism to aggregate

A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange

by the mean teacher framework taking advantage of the cross-scale self-attention mechanism in Deformable

the next actions while conforming to social behaviors by engaging to joint attention. Our key innovation is

a joint sequence rather than a time sequence, allowing us to perform attention over joints while predicting

to synthesize pixels. Because keypoints can contain errors, we propose a cross-modal attention scheme to select

to be challenging enough for even human players. Past approaches have not paid enough attention to the

to successfully execute its current chore. Filtering distracting inputs that contain irrelevant data has received little attention in the

Transformers -- the emergence of high-norm tokens that lead to noisy attention maps. We observe that

HTML (experimental) Abstract: Transformers today still struggle to generate one-minute videos because self-attention layers are inefficient for

propose M3 with key components of principal scene components and Gaussian memory attention, enabling efficient training and

propose M3 with key components of principal scene components and Gaussian memory attention, enabling efficient training and

and Distributed Version Control. A barrier to feedback that deserves greater attention is Schema Evolution. When

close. In this paper, we study the tight interplay between our momentary visual attention and motor

point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the

Chen and 4 other authors View PDF Abstract: Multi-view image generation attracts particular attention these days due to

from a single image has recently attracted a lot of attention, and it

from identity-relevant ReID features ('Color Ignore'). To achieve this, we introduce S2A self-attention, a novel

of the previous clip at the front of the attention window as conditioning, which

HTML (experimental) Abstract: Transformers today still struggle to generate one-minute videos because self-attention layers are inefficient for

is a challenging task that requires inferring the camera wearer's visual attention, and decoding