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- CvT: Introducing Convolutions to Vision Transformers2,569
A new architecture is presented that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both de-signs, and the positional encoding, a crucial component in existing Vision Transformers, can be safely re-moved in this model.
- Dynamic Convolution: Attention Over Convolution Kernels1,508
By simply using dynamic convolution for the state-of-the-art architecture MobileNetV3-Small, the top-1 accuracy of ImageNet classification is boosted by 2.9% with only 4% additional FLOPs and 2.8 AP gain is achieved on COCO keypoint detection.
- Dynamic Head: Unifying Object Detection Heads with Attentions1,044
This paper presents a novel dynamic head framework to unify object detection heads with attentions by coherently combining multiple self-attention mechanisms between feature levels for scale- awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness that significantly improves the representation ability of object detection Heads without any computational overhead.
- RegionCLIP: Region-based Language-Image Pretraining1,000
A new method is proposed that signifi-cantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts, and the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets.
- Mobile-Former: Bridging MobileNet and Transformer727
The proposed light-weight cross attention to model the bridge enables bidirectional fusion of local and global features and builds an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR but saves 52% of computational cost and 36% of parameters.
- Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks657
Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks, is introduced and demonstrated to be a strong vision foundation model contender with un-precedented zero-shot and fine-tuning capabilities.
- Rewrite the Stars621
This study attempts to reveal the star operation's ability of mapping inputs into high-dimensional, non-linear feature spaces-akin to kernel tricks-without widening the network.
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- Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding405
A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work, on a range of vision tasks, including image classification, object detection, and segmentation.
- Generalized Decoding for Pixel, Image, and Language404
X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks with strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings.
- MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment351
This paper presents Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network and proposes to explicitly model moment-wise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner.
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- BEVT: BERT Pretraining of Video Transformers279
BEVT is introduced which decouples video representation learning into spatial representation learning and temporal dynamics learning and achieves state-of-the-art performance on three challenging video benchmarks where BEVT achieves very promising results.
- Temporal Context Network for Activity Localization in Videos271
A Temporal Context Network (TCN) for precise temporal localization of human activities which outperforms state-of-the-art methods on the ActivityNet dataset and the THU-MOS14 dataset.
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- Dynamic ReLU226
This paper proposes dynamic ReLU (DY-ReLU), a dynamic rectifier of which parameters are generated by a hyper function over all in-put elements, and encodes the global context into the hyper function, and adapts the piecewise linear activation function accordingly.
- Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning135
This paper proposes masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning that demonstrates that video transformers pre-trained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets.
- MicroNet: Improving Image Recognition with Extremely Low FLOPs113
This paper found that two factors, sparse connectivity and dynamic activation function, are effective to improve the accuracy and proposes micro-factorized convolution, which factorizes a convolution matrix into low rank matrices, to integrate sparse connectivity into convolution.
- Reduce Information Loss in Transformers for Pluralistic Image Inpainting104
A patch-based auto-encoder P-VQVAE, where the encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged is designed.
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- ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System79
The vision for multimodal and versatile video understanding is presented and a prototype system, built upon a tracklet-centric paradigm, which treats tracklets as the basic video unit and employs various Video Foundation Models to annotate their properties e.g., appearance, motion, etc.
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- Look Before You Match: Instance Understanding Matters in Video Object Segmentation61
It is argued that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, i.e., identifying and segmenting object instances within the video.
- Residual Mixture of Experts58
Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as segmentation and detection, achieves comparable results with the upper-bound MoE training, while only introducing minor additional training cost than the lower-bound non-MoE training pipelines.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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