2 citations · 5 across the 29 of their papers we have counts for
31 papers
Does Continual Imitation Learning Remain Grounded? A Language-Perturbed Benchmark for Robotic Task Retention
Siddeshwar Raghavan, Ziqin Yuan, Fengqing Zhu +1
Continual imitation learning evaluates whether a robot can learn new knowledge without forgetting previously learned skills. However, retaining task performance does not ensure the…
Decoder-Guided Lossy Contour Coding Via Anchor Refinement
Ruoyu Yang, Yichi Zhang, Haohong Wang +1
Object contours serve as compact structural priors for many receiver-side vision tasks such as image super-resolution, edge-conditioned generation, and machine vision. When such ta…
Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
Gautham Vinod, Bruce Coburn, Siddeshwar Raghavan +1
Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logistics, and smart health. Exist…
Can You Hear, Localize, and Segment Continually? An Exemplar-Free Continual Learning Benchmark for Audio-Visual Segmentation
Siddeshwar Raghavan, Gautham Vinod, Bruce Coburn +1
Audio-Visual Segmentation (AVS) aims to produce pixel-level masks of sound producing objects in videos, by jointly learning from audio and visual signals. However, real-world envir…
Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression
Yichi Zhang, Ruoyu Yang, Fengqing Zhu
Recent advances in learned video compression (LVC) have led to significant performance gains, with codecs such as DCVC-RT surpassing the H.266/VVC low-delay mode in compression eff…
Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
Jinge Ma, Fengqing Zhu
With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distribution…
PANDA -- Patch And Distribution-Aware Augmentation for Long-Tailed Exemplar-Free Continual Learning
Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
Exemplar-Free Continual Learning (EFCL) restricts the storage of previous task data and is highly susceptible to catastrophic forgetting. While pre-trained models (PTMs) are increa…
Flexible Mixed Precision Quantization for Learned Image Compression
Md Adnan Faisal Hossain, Zhihao Duan, Fengqing Zhu
Despite its improvements in coding performance compared to traditional codecs, Learned Image Compression (LIC) suffers from large computational costs for storage and deployment. Mo…
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
Jiangpeng He, Zhihao Duan, Fengqing Zhu
Class-Incremental Learning (CIL) aims to learn new classes sequentially while retaining the knowledge of previously learned classes. Recently, pre-trained models (PTMs) combined wi…
Low-Rank Adaptation of Pre-trained Vision Backbones for Energy-Efficient Image Coding for Machine
Yichi Zhang, Zhihao Duan, Yuning Huang +1
Image Coding for Machines (ICM) focuses on optimizing image compression for AI-driven analysis rather than human perception. Existing ICM frameworks often rely on separate codecs f…
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
Yichi Zhang, Zhihao Duan, Yuning Huang +1
As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in acceler…
Robust3D-CIL: Robust Class-Incremental Learning for 3D Perception
Jinge Ma, Jiangpeng He, Fengqing Zhu
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models must continuously adapt…
Balanced Rate-Distortion Optimization in Learned Image Compression
Yichi Zhang, Zhihao Duan, Yuning Huang +1
Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced upd…
MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds
Jinge Ma, Xiaoyan Zhang, Gautham Vinod +3
Food portion estimation is crucial for monitoring health and tracking dietary intake. Image-based dietary assessment, which involves analyzing eating occasion images using computer…
High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
Ming Lu, Zhihao Duan, Wuyang Cong +3
The enhanced Deep Hierarchical Video Compression-DHVC 2.0-has been introduced. This single-model neural video codec operates across a broad range of bitrates, delivering not only s…
MetaFood3D: 3D Food Dataset with Nutrition Values
Yuhao Chen, Jiangpeng He, Gautham Vinod +11
Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets…
FMiFood: Multi-modal Contrastive Learning for Food Image Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is the fundamental step in image-based dietary assessment, which aims to estimate participants' nutrient intake from eating occasion images. A common chal…
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Jiangpeng He, Yuhao Chen, Gautham Vinod +16
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…
On Efficient Neural Network Architectures for Image Compression
Yichi Zhang, Zhihao Duan, Fengqing Zhu
Recent advances in learning-based image compression typically come at the cost of high complexity. Designing computationally efficient architectures remains an open challenge. In t…
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor
Yuning Huang, Mohamed Abul Hassan, Jiangpeng He +6
Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging prob…
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Fiona R. Kolbinger, Jiangpeng He, Jinge Ma +1
Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision…
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Justin Yang, Zhihao Duan, Jiangpeng He +1
Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition s…
DELTA: Decoupling Long-Tailed Online Continual Learning
Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu
A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows…
Food Portion Estimation via 3D Object Scaling
Gautham Vinod, Jiangpeng He, Zeman Shao +1
Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods. However, accurate portion estimation remains a major chal…