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Works281 from public data
- 1,512
- 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.
- 1,488
- The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)1,017
This paper analyzes the latest model, GPT-4V(ision), to deepen the understanding of large multimodal models, and curate and organize a collection of carefully designed qualitative samples spanning a variety of domains and tasks to inspire future research on the next-generation multi-sensory task formulation.
- 984
- End-to-End Human Pose and Mesh Reconstruction with Transformers813
A new method to reconstruct 3D human pose and mesh vertices from a single image using a transformer encoder to jointly model vertex-vertex and vertex-joint interactions, which generates new state-of-the-art results for human mesh reconstruction on the public Human3.6M and 3DPW datasets.
- GIT: A Generative Image-to-text Transformer for Vision and Language808
This paper design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering and establishes new state of the arts on 12 challenging benchmarks with a large margin.
- 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.
- Rethinking Classification and Localization for Object Detection712
A Double-Head method is proposed, which has a fully connected head focusing on classification and a convolution head for bounding box regression, and it is found that fc-head has more spatial sensitivity than conv-head.
- End-to-End Semi-Supervised Object Detection with Soft Teacher698
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods, and proposes two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network.
- MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action649
This paper defines and explores a comprehensive list of advanced vision tasks that are intriguing to solve, but may exceed the capabilities of existing vision and vision-language models and proposes MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action.
- An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA559
This work proposes PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA, and treats GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge.
- Agent Laboratory: Using LLM Agents as Research Assistants534
This work introduces Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process, and finds that it significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods.
- 531
- Robust 3D Action Recognition with Random Occupancy Patterns501
This work extracts semi-local features called random occupancy pattern ROP features, which employ a novel sampling scheme that effectively explores an extremely large sampling space and utilizes a sparse coding approach to robustly encode these features.
- An Empirical Study of Training End-to-End Vision-and-Language Transformers482
Meter, a Multimodal End-to-end TransformER framework, is presented, through which it is investigated how to design and pre-train a fully transformer-based VL model in an end- to-end manner and provides insights on how to train a performant VL transformer.
- Mesh Graphormer444
Experimental results show that the proposed method, Mesh Graphormer, significantly outperforms the previous state-of-the-art methods on multiple benchmarks, including Human3.6M, 3DPW, and FreiHAND datasets.
- HP-GAN: Probabilistic 3D Human Motion Prediction via GAN384
A novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which the model learns a probability density function of future human poses conditioned on previous poses.
- SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning356
SwinBERT is an end-to-end transformer-based model for video captioning, which takes video frame patches directly as inputs, and outputs a natural language description, and adopts a video transformer to encode spatial-temporal representations that can adapt to variable lengths of video input without dedicated design for different frame rates.
- 344
- Scaling Up Vision-Language Pretraining for Image Captioning342
LEMON O, a LargE-scale iMage captiONer, is presented, and the first empirical study on the scaling behavior of VLP for image captioning is provided, and it is shown LEMON can generate captions with long-tail vi-sual concepts when used in a zero-shot manner.
- 326
- 318
- 318
- STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences294
This paper presents Space-Time Occupancy Patterns (STOP), a new visual representation for 3D action recognition from sequences of depth maps that preserves spatial and temporal contextual information between space-time cells while being flexible enough to accommodate intra-action variations.
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 2026-10-13. 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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