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Works130 from public data
- VGGFace2: A Dataset for Recognising Faces across Pose and Age3,189
A new large-scale face dataset named VGGFace2 is introduced, which contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject, and the automated and manual filtering stages to ensure a high accuracy for the images of each identity are described.
- Vggsound: A Large-Scale Audio-Visual Dataset992
The goal is to collect a large-scale audio-visual dataset with low label noise from videos ‘in the wild’ using computer vision techniques and investigates various Convolutional Neural Network architectures and aggregation approaches to establish audio recognition baselines for this new dataset.
- Voxceleb: Large-scale speaker verification in the wild887
A very large-scale audio-visual dataset collected from open source media using a fully automated pipeline and developed and compared different CNN architectures with various aggregation methods and training loss functions that can effectively recognise identities from voice under various conditions are introduced.
- Prompting Visual-Language Models for Efficient Video Understanding539
A simple but strong baseline is presented to efficiently adapt the pre-trained I-VL model, and exploit its powerful ability for resource-hungry video understanding tasks, with minimal training, to optimise a few random vectors that convert video-related tasks into the same format as thePre-training objectives.
- Microscopy cell counting and detection with fully convolutional regression networks505
A new state-of-the-art performance for cell count on standard synthetic image benchmarks is set and it is shown that the FCRNs trained entirely with synthetic data can generalise well to real microscopy images both for cell counting and detections for the case of overlapping cells.
- PMC-LLaMA: toward building open-source language models for medicine474
The proposed PMC-LLaMA model can promote further development of foundation models in medicine, serving as a medical trainable basic generative language backbone, and contribute a large-scale, comprehensive dataset for instruction tuning.
- PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering408
This study reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction and proposes a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model.
- Self-supervised Co-Training for Video Representation Learning405
This paper investigates the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation (InfoNCE) training, and proposes a novel self-supervised co-training scheme to improve the popular infoNCE loss.
- Video Representation Learning by Dense Predictive Coding403
With single stream (RGB only), DPC pretrained representations achieve state-of-the-art self-supervised performance on both UCF101 and HMDB51, outperforming all previous learning methods by a significant margin, and approaching the performance of a baseline pre-trained on ImageNet.
- Utterance-level Aggregation for Speaker Recognition in the Wild375
This paper proposes a powerful speaker recognition deep network, using a ‘thin-ResNet’ trunk architecture, and a dictionary-based NetVLAD or GhostVLAD layer to aggregate features across time, that can be trained end-to-end.
- Memory-Augmented Dense Predictive Coding for Video Representation Learning266
A new architecture and learning framework Memory-augmented Dense Predictive Coding (MemDPC) is proposed for the self-supervised learning from video, in particular for representations for action recognition, trained with a predictive attention mechanism over the set of compressed memories.
- MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training262
This paper adopts a novel triplet extraction module to extract the medical-related information, avoiding unnecessary complexity from language grammar and enhancing the supervision signals, and proposes a novel triplet encoding module with entity translation by querying a knowledge base.
- A Sanity Check for AI-generated Image Detection256
A sanity check on whether the task of AI-generated image detection has been solved is conducted, and AIDE (AI-generated Image DEtector with Hybrid Features), which leverages multiple experts to simultaneously extract visual artifacts and noise patterns is proposed.
- Localizing Visual Sounds the Hard Way252
The key technical contribution is to show that, by training the network to explicitly discriminate challenging image fragments, even for images that do contain the object emitting the sound, the authors can significantly boost the localization performance by introducing a mechanism to mine hard samples and add them to a contrastive learning formulation automatically.
- PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images245
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations, and the proposed detector, termed as PromptDet, shows superior performance over existing approaches with fewer additional training images andzero manual annotations whatsoever.
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- MAST: A Memory-Augmented Self-Supervised Tracker207
A dense tracking model trained on videos without any annotations is proposed that surpasses previous self-supervised methods on existing benchmarks by a significant margin, and achieves performance comparable to supervised methods.
- Self-supervised Video Object Segmentation by Motion Grouping202
A simple variant of the Transformer is introduced to segment optical flow frames into primary objects and the background, which can be trained in a self-supervised manner, i.e. without using any manual annotations, and achieves superior results compared to previous state-of-the-art self- supervised methods on public benchmarks.
- Class-Agnostic Counting201
The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images, when training on the entire dataset, and outperforms all previous methods by a large margin.
- Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval196
Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks with a simple and elegant implementation and improves the performance over the state-of-the-art, especially for larger-scale datasets, thus demonstrating the effectiveness and scalability of Smooth-AP to real-world scenarios.
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- PMC-LLaMA: Towards Building Open-source Language Models for Medicine178
Preliminary evaluations are conducted on three biomedical QA datasets, showing that the PMC-LLaMA model after netuning demonstrates better understanding of biomedical domain-specific concepts, thus achieving high performance on QA benchmarks.
- WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs170
The first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs, is introduced and the experimental results indicate that existing models face significant challenges in understanding real-world scenarios.
- Collaboration Helps Camera Overtake LiDAR in 3D Detection159
The proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication, and shows a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios.
- CounTR: Transformer-based Generalised Visual Counting151
This paper introduces a novel transformer-based architecture for generalised visual object counting, termed as Counting Transformer (CounTR), which explicitly capture the similarity between image patches or with given"exemplars" with the attention mechanism.
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 2026-10-16. 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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