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Found 13 papers, 8 papers with code
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
Learning Versatile Neural Architectures by Propagating Network Codes
(4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i. e., multitask neural architectures and architecture transferring between different tasks.
Towards artificial general intelligence via a multimodal foundation model
To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.
Compressed Video Contrastive Learning
To enhance the representation ability of the motion vectors, hence the effectiveness of our method, we design a cross guidance contrastive learning algorithm based on multi-instance InfoNCE loss, where motion vectors can take supervision signals from RGB frames and vice versa.
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval
Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10, 800X faster in inference) w. r. t.
Ranked #23 on
Video Retrieval
on MSR-VTT
Multimodal foundation models are better simulators of the human brain
Further, from the perspective of neural encoding (based on our foundation model), we find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones.
LGDN: Language-Guided Denoising Network for Video-Language Modeling
However, this hypothesis often fails for two reasons: (1) With the rich semantics of video contents, it is difficult to cover all frames with a single video-level description; (2) A raw video typically has noisy/meaningless information (e. g., scenery shot, transition or teaser).
Monolingual Recognizers Fusion for Code-switching Speech Recognition
It has two stages: the speech awareness (SA) stage and the language fusion (LF) stage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49. 7% recall@1 with 2. 2% model parameters, outperforming the latest competitors by 2. 0%.
VDT: General-purpose Video Diffusion Transformers via Mask Modeling
We also propose a unified spatial-temporal mask modeling mechanism, seamlessly integrated with the model, to cater to diverse video generation scenarios.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
DeepSeek-VL: Towards Real-World Vision-Language Understanding
The DeepSeek-VL family (both 1. 3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks.
Ranked #28 on
Visual Question Answering
on MM-Vet
