22 accepted papers
Diffusion-based video editing has emerged as an important paradigm for high-quality and flexible content generation. However, despite their generality and strong modeling capacity, Diffusion Transformers (DiT) remain computationally expensive due to the iterative denoising process, posing challenges
Stochastic Human Motion Prediction (HMP) has become an essential task for the realm of computer vision, for its capacity to anticipate accurate and diverse future human trajectories. Current diffusion-based techniques typically enforce skeletal consistency by encoding structural priors into network…
Recent Mamba-based methods for the pose-lifting task tend to model joint dependencies by 2D-to-1D mapping with diverse scanning strategies. Though effective, they struggle to model intricate joint connections and uniformly process all joint motion trajectories while neglecting the intrinsic differen…
Online stereo rectification is critical for autonomous vehicles and robots in dynamic environments, where factors such as vibration, temperature fluctuations, and mechanical stress can affect rectification accuracy and severely degrade downstream stereo depth estimation. Current dominant approaches…
Diagram Question Answering (DQA) is a challenging task requiring models to answer natural language questions based on visual diagram contexts. It serves as a crucial basis for academic tutoring technical support and more practical applications. DQA poses significant challenges such as the demand for…
Existing human object interaction (HOI) detection methods have introduced zero-shot learning techniques to recognize unseen interactions, but they still have limitations in understanding context information and comprehensive reasoning. To overcome these limitations, we propose a novel HOI learning f…
In recent years, the rapid increase in online video content has underscored the limitations of static Video Question Answering (VideoQA) models trained on fixed datasets, as they struggle to adapt to new questions or tasks posed by newly available content. In this paper, we explore the novel challen…
Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems. In real-world applications, it is common for vehicle cabins to be equipped with cameras featuring different modalities. However, multi-modality fusion strategies for the DAR task within car cabins have rarely been studie…
In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream. The bit errors bring unpredictable color casts and block shifts on decoded image contents, which cannot be trivially resolved by existing image restoration methods mainly relying on pre-de…
The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment. However, they typically simulate the missing content by manual-designed error masks, thus failing to fill in the realistic video loss in video communica…
In this paper, we investigate an open research task of generating controllable 3D textured shapes from the given textual descriptions. Previous works either require ground truth caption labeling or extensive optimization time. To resolve these issues, we present a novel framework, TAPS3D, to train a…
It is difficult to construct a data collection including all possible combinations of human actions and interacting objects due to the combinatorial nature of human-object interactions (HOI). In this work, we aim to develop a transferable HOI detector for unseen interactions. Existing HOI detectors…
Visual food recognition is emerging as an important application in dietary monitoring and management in recent years. Existing works use large backbone networks to achieve good performance. However, these networks are not able to be deployed on personal portable devices due to large size and computa…
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories. Previous HOI studies are mainly conducted in the regime of limit object categories (e.g., 80 categories). Their solutions may face new difficulties in both object detection and int…
We aim to detect human interactions with novel objects through zero-shot learning. Different from previous works, we allow unseen object categories by using its semantic word embedding. To do so, we design a human-object region proposal network specifically for the human-object interaction detection…
This paper proposes dynamically modulated metric learning (DMML) for learning a tiered similarity space to perform visual search. Existing methods often treat the training samples having different degree of information with equal importance which hinders in capturing the underlying granularities in…
Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. Using advanced depth sensors, collection of large scale 3D dataset is no longer a cumbersome process. However,…
This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image conten…
This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts…
We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the…
Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones. However, it does not explicitly model feature co-occurrences in samples. In this paper, we propose a no…
The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradie…