Search Results for author:
Found 6 papers, 6 papers with code
ST-LLM: Large Language Models Are Effective Temporal Learners
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding
Large-scale image-language pretrained models, e. g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data.
One For All: Video Conversation is Feasible Without Video Instruction Tuning
Without bells and whistles, BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours.
Ranked #5 on
Zero-Shot Video Retrieval
on LSMDC
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+6
Causality Compensated Attention for Contextual Biased Visual Recognition
Visual attention does not always capture the essential object representation desired for robust predictions.
Ranked #1 on
Multi-Label Image Classification
on MSCOCO
Multi-Label Classification
Multi-Label Image Classification
+1
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
In this paper, based on the CLIP model, we revisit temporal modeling in the context of image-to-video knowledge transferring, which is the key point for extending image-text pretrained models to the video domain.
Ranked #7 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Contextual Debiasing for Visual Recognition With Causal Mechanisms
As a common problem in the visual world, contextual bias means the recognition may depend on the co-occurrence context rather than the objects themselves, which is even more severe in multi-label tasks due to multiple targets and the absence of location.
Ranked #9 on
Multi-Label Classification
on MS-COCO
