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- 1,399
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- Learning Spatio-Temporal Transformer for Visual Tracking1,225
A new tracking architecture with an encoder-decoder transformer as the key component, which models the global spatio-temporal feature dependencies between target objects and search regions, while the decoder learns a query embedding to predict the spatial positions of the target objects.
- 1,004
- Learning Texture Transformer Network for Image Super-Resolution965
A novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively, which achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.
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- Ocean: Object-Aware Anchor-Free Tracking841
This paper introduces a feature alignment module to learn an object-aware feature from predicted bounding boxes that can further contribute to the classification of target objects and background and presents a novel tracking framework based on the anchor-free model.
- Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language640
A Temporal Adjacent Network (2D-TAN) is proposed, a single-shot framework for moment localization that is capable of encoding the adjacent temporal relation, while learning discriminative features for matching video moments with referring expressions.
- Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting500
This paper proposes a Pyramid-context Encoder Network for image inpainting by deep generative models, built upon a U-Net structure with three tailored components, ie.
- 496
- Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition488
TASN consists of a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, an attention-based sampler which highlights attended parts with high resolution, and a feature distiller, which distills part features into an object-level feature by weight sharing and feature preserving strategies.
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation484
A new advanced VLA architecture derived from VLM that has a specialized action module conditioned on VLM output is presented that not only significantly surpasses existing VLAs in task performance and but also exhibits remarkable adaptation to new robots and generalization to unseen objects and backgrounds.
- Rethinking and Improving Relative Position Encoding for Vision Transformer467
New relative position encoding methods dedicated to 2D images, called image RPE (iRPE), are proposed, which consider directional relative distance modeling as well as the interactions between queries and relative position embeddings in self-attention mechanism.
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- AutoFormer: Searching Transformers for Visual Recognition366
This work proposes a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search, which surpass the recent state-of-the-arts such as ViT and DeiT and achieves top-1 accuracy on ImageNet.
- Learning Joint Spatial-Temporal Transformations for Video Inpainting365
This paper simultaneously fill missing regions in all input frames by self-attention, and proposes to optimize STTN by a spatial-temporal adversarial loss to show the superiority of the proposed model.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation337
The first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos is proposed, with a novel Multi-Modal Diffusion model with two-coupled denoising autoencoders.
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- Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning318
This paper proposes SOHO to "Seeing Out of tHe bOx" that takes a whole image as input, and learns vision-language representation in an end-to-end manner, and does not require bounding box annotations which enables inference 10 times faster than region-based approaches.
- Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions293
A novel High-resolution and Diversified VIdeo-LAnguage pre-training model (HD-VILA) for many visual tasks that outperform SOTA models with relative increases and achieves new state-of-the-art results in 10 VL understanding tasks and 2 more novel text-to-visual generation tasks.
- LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search265
This work presents LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ and Ocean, while using much fewer model Flops and parameters.
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- Revisiting Anchor Mechanisms for Temporal Action Localization215
A novel anchor-free action localization module that represents an action instance as a point with its distances to the starting boundary and ending boundary, alleviating the pre-defined anchor restrictions in terms of action localization and duration is proposed.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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