25 accepted papers
Multi-camera 3D object detection (MC3D) has attracted increasing attention with the growing deployment of multi-sensor physical agents, such as robots and autonomous vehicles. However, MC3D models still struggle to generalize to unseen platforms with new multi-camera configurations. Current solution
We propose Intrinsic Quality (IQ), a validation-free metric designed to estimate the inherent potential of face recognition (FR) datasets to produce high-performance models without the need for full-scale training. IQ integrates two components: (i) a Neighbor-Consistency Score that quantifies local …
Recent advances in multimodal large language models (MLLM) for audio music have demonstrated strong capabilities in music understanding, yet symbolic music, a fundamental representation of musical structure, remains unexplored. In this work, we introduce MIDI-LLaMA, the first instruction-following M…
Multi-view 3D detection with bird’s eye view (BEV) is crucial for autonomous driving and robotics, but its robustness in real-world is limited as it struggles to predict accurate depth values. A mainstream solution, cross-modal distillation, transfers depth information from LiDAR to camera models bu
Cross-modal knowledge distillation (CMKD) in monocular 3D object detection transfers LiDAR’s accurate depth information to compensate for the limitations of camera model. However, current methods directly align the intermediate features of the teacher and student networks, in which the modality gap
Previous animatronic faces struggle to effectively express emotions due to both hardware and software limitations. On the hardware side, earlier approaches either used rigid-driven mechanisms, which provide precise control but are difficult to design within constrained spaces, or tendon-driven mecha…
The differences in imaging devices cause multimodal images to have modal differences and geometric distortions, complicating the matching task. Deep learning-based matching methods struggle with multimodal images due to the lack of large annotated multimodal datasets. To address these challenges, we
Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains challenging. We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts va
Generalizable depth completion enables the acquisition of dense metric depth maps for unseen environments, offering robust perception capabilities for various downstream tasks. However, training such models typically requires large-scale datasets with metric depth labels, which are often labor-inten…
Few-shot class-incremental learning (FSCIL) presents a significant challenge in machine learning, requiring models to integrate new classes from limited examples while preserving performance on previously learned classes. Recently, prompt-based CIL approaches leverage ample data to train prompts, ef…
Weakly Supervised Object Localization (WSOL) is a challenging task, which aims to learn object localization with less costly image-level labels. Existing convolution neural network (CNN) based methods tend to focus on discriminative regions of objects, while transformer-based methods overemphasize d…
One of the major difficulties in the Visual Question Answering (VQA) task of real-world images is the long-tailed distribution of concepts which makes the model vulnerable to negative linguistic biases. To imitate human learning and reasoning, researchers have designed reasoning models, which, howev…
Visual Question Answering based on external Knowledge Bases(KB-VQA) have gained more attention in recent years. Due to the large scale of knowledge base data and the lack of manual annotation information in the process of retrieving knowledge for image problem data, the current model usually suffers…
Abductive natural language commonsense reasoning is a task aiming at inferring the most plausible explanation in narrative text for observed events. Previous works mostly concentrate on utilizing powerful pre-trained language models and making better use of excess training data to learn abundant eve…
Emotion recognition in conversation (ERC) has received extensive attention for its wide applications in recent years. Considering the actual situation, we focus on the real-time conversation scenarios, in which how to model the conversation emotion with only the historical contextual information and…
Dialogue Aspect-based Sentiment Quadruple analysis (DiaASQ) extends ABSA to more complex real-world scenarios (i.e., dialogues), which makes existing generation methods encounter heightened noise and order bias challenges, leading to decreased robustness and accuracy.To address these, we propose the…
Recent advancements in 3D perception have led to a proliferation of network architectures, particularly those involving multi-modal fusion algorithms. While these fusion algorithms improve accuracy, their complexity often impedes real-time performance. This paper introduces VeXKD, an effective and V…
The transformer model is known to be computationally demanding, and prohibitively costly for long sequences, as the self-attention module uses a quadratic time and space complexity with respect to sequence length. Many researchers have focused on designing new forms of self-attention or introducing…
ASTE (Aspect Sentiment Triplet Extraction) has gained increasing attention. Recent advancements in the ASTE task have been primarily driven by Natural Language Generation-based (NLG) approaches. However, most NLG methods overlook the supervision of the encoder-decoder hidden representations and fail…
The scarcity of labeled data and the problem of model overfitting have been the challenges in few-shot learning. Recently, semi-supervised few-shot learning has been developed to obtain pseudo-labels of unlabeled samples for expanding the support set. However, the relationship between unlabeled and…
Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span extraction in a more direct and interpretable way. In this paper, we propose a novel model of B…
Weakly supervised object localization (WSOL) aims to localize object with only weak supervision like image-level labels. However, a long-standing problem for available techniques based on the classification network is that they often result in highlighting the most discriminative parts rather than t…
While existing works have explored a variety of techniques to push the envelop of weakly-supervised semantic segmentation, there is still a significant gap compared to the supervised methods. In real-world application, besides massive amount of weakly-supervised data there are usually a few availabl…
Image-based virtual try-on systems for fitting new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. A desirable pipeline should not only transform the target clothes into the most fitting shape seamlessly but also preserve well the clothes i…
Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). Despite its simplicity, popularity and excellent performance, the component does not explicitly encourage discriminative learning of features. In this pape…