177 citations · 826 across the 39 of their papers we have counts for
18 papers · 1 filter
Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
Liangliang Cao, Bowen Zhang, Chen Chen +5
The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of m…
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao +7
Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, re…
Exploiting Category Names for Few-Shot Classification with Vision-Language Models
Taihong Xiao, Zirui Wang, Liangliang Cao +3
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks. Notably, many vision-language models build two encoder…
Spatial-Temporal Alignment Network for Action Recognition and Detection
Junwei Liang, Liangliang Cao, Xuehan Xiong +2
This paper studies how to introduce viewpoint-invariant feature representations that can help action recognition and detection. Although we have witnessed great progress of action…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
Progressive Learning Algorithm for Efficient Person Re-Identification
Zhen Li, Hanyang Shao, Nian Xue +2
This paper studies the problem of Person Re-Identification (ReID)for large-scale applications. Recent research efforts have been devoted to building complicated part models, which…
Product Image Recognition with Guidance Learning and Noisy Supervision
Qing Li, Xiaojiang Peng, Liangliang Cao +3
This paper considers recognizing products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category dive…
Automatic adaptation of object detectors to new domains using self-training
Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh +4
This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readil…
Focal Visual-Text Attention for Visual Question Answering
Junwei Liang, Lu Jiang, Liangliang Cao +2
Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question a…
Improving Object Detection from Scratch via Gated Feature Reuse
Zhiqiang Shen, Honghui Shi, Jiahui Yu +7
In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We…
Lip2AudSpec: Speech reconstruction from silent lip movements video
Hassan Akbari, Himani Arora, Liangliang Cao +1
In this study, we propose a deep neural network for reconstructing intelligible speech from silent lip movement videos. We use auditory spectrogram as spectral representation of sp…
MemexQA: Visual Memex Question Answering
Lu Jiang, Junwei Liang, Liangliang Cao +3
This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory abou…
Learning from Noisy Labels with Distillation
Yuncheng Li, Jianchao Yang, Yale Song +3
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally,…
Image Based Appraisal of Real Estate Properties
Quanzeng You, Ran Pang, Liangliang Cao +1
Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. T…
Video2GIF: Automatic Generation of Animated GIFs from Video
Michael Gygli, Yale Song, Liangliang Cao
We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video t…
GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring
Wenying Ma, Liangliang Cao, Lei Yu +2
Fisher vector has been widely used in many multimedia retrieval and visual recognition applications with good performance. However, the computation complexity prevents its usage in…
TGIF: A New Dataset and Benchmark on Animated GIF Description
Yuncheng Li, Yale Song, Liangliang Cao +4
With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich metadata. To advance research on animated GIF understanding, we collecte…
Learning Latent Spatio-Temporal Compositional Model for Human Action Recognition
Xiaodan Liang, Liang Lin, Liangliang Cao
Action recognition is an important problem in multimedia understanding. This paper addresses this problem by building an expressive compositional action model. We model one action…