pwc-archive/papers-with-abstracts huggingface dataset:
Improving 6D Object Pose Estimation of metallic Household and Industry Objects
Authors: Thomas Pöllabauer, Michael Gasser, Tristan Wirth, Sarah Berkei, Volker Knauthe, Arjan Kuijper
Date: 2025-03-05 00:00:00
Tasks: 6D Pose Estimation using RGB, Pose Estimation, Scene Understanding

6D object pose estimation suffers from reduced accuracy when applied to metallic objects. We set out to improve the state-of-the-art by addressing challenges such as reflections and specular highlights in industrial applications. Our novel BOP-compatible dataset, featuring a diverse set of metallic objects (cans, household, and industrial items) under various lighting and background conditions, provides additional geometric and visual cues. We demonstrate that these cues can be effectively leveraged to enhance overall performance. To illustrate the usefulness of the additional features, we improve upon the GDRNPP algorithm by introducing an additional keypoint prediction and material estimator head in order to improve spatial scene understanding. Evaluations on the new dataset show improved accuracy for metallic objects, supporting the hypothesis that additional geometric and visual cues can improve learning.
LION-FS: Fast & Slow Video-Language Thinker as Online Video Assistant
Authors: Wei Li, Bing Hu, Rui Shao, Leyang Shen, Liqiang Nie
Date: 2025-03-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Response Generation

First-person video assistants are highly anticipated to enhance our daily lives through online video dialogue. However, existing online video assistants often sacrifice assistant efficacy for real-time efficiency by processing low-frame-rate videos with coarse-grained visual features.To overcome the trade-off between efficacy and efficiency, we propose "Fast & Slow Video-Language Thinker" as an onLIne videO assistaNt, LION-FS, achieving real-time, proactive, temporally accurate, and contextually precise responses. LION-FS adopts a two-stage optimization strategy: 1)Fast Path: Routing-Based Response Determination evaluates frame-by-frame whether an immediate response is necessary. To enhance response determination accuracy and handle higher frame-rate inputs efficiently, we employ Token Aggregation Routing to dynamically fuse spatiotemporal features without increasing token numbers, while utilizing Token Dropping Routing to eliminate redundant features. 2)Slow Path: Multi-granularity Keyframe Augmentation optimizes keyframes during response generation. To provide comprehensive and detailed responses beyond atomic actions constrained by training data, fine-grained spatial features and human-environment interaction features are extracted through multi-granular pooling. These features are further integrated into a meticulously designed multimodal Thinking Template to guide more precise response generation. Comprehensive evaluations on online video tasks demonstrate that LION-FS achieves state-of-the-art efficacy and efficiency.
A Generative Approach to High Fidelity 3D Reconstruction from Text Data
Authors: Venkat Kumar R, Deepak Saravanan
Date: 2025-03-05 00:00:00
Tasks: 3D Reconstruction, Image Generation, Reflection Removal, Text to Image Generation, Text-to-Image Generation

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep learning methods for reflection removal and 3D reconstruction. By leveraging state-of-the-art generative models like Stable Diffusion, the methodology translates natural language inputs into detailed 3D models through a multi-stage workflow. The reconstruction process begins with the generation of high-quality images from textual prompts, followed by enhancement by a reinforcement learning agent and reflection removal using the Stable Delight model. Advanced image upscaling and background removal techniques are then applied to further enhance visual fidelity. These refined two-dimensional representations are subsequently transformed into volumetric 3D models using sophisticated machine learning algorithms, capturing intricate spatial relationships and geometric characteristics. This process achieves a highly structured and detailed output, ensuring that the final 3D models reflect both semantic accuracy and geometric precision. This approach addresses key challenges in generative reconstruction, such as maintaining semantic coherence, managing geometric complexity, and preserving detailed visual information. Comprehensive experimental evaluations will assess reconstruction quality, semantic accuracy, and geometric fidelity across diverse domains and varying levels of complexity. By demonstrating the potential of AI-driven 3D reconstruction techniques, this research offers significant implications for fields such as augmented reality (AR), virtual reality (VR), and digital content creation.
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
Authors: Nianzu Yang, Pandeng Li, Liming Zhao, Yang Li, Chen-Wei Xie, Yehui Tang, Xudong Lu, Zhihang Liu, Yun Zheng, Yu Liu, Junchi Yan
Date: 2025-03-05 00:00:00
Tasks: Decoder, Video Compression, Video Generation, Video Reconstruction

Existing video tokenizers typically use the traditional Variational Autoencoder (VAE) architecture for video compression and reconstruction. However, to achieve good performance, its training process often relies on complex multi-stage training tricks that go beyond basic reconstruction loss and KL regularization. Among these tricks, the most challenging is the precise tuning of adversarial training with additional Generative Adversarial Networks (GANs) in the final stage, which can hinder stable convergence. In contrast to GANs, diffusion models offer more stable training processes and can generate higher-quality results. Inspired by these advantages, we propose CDT, a novel Conditioned Diffusion-based video Tokenizer, that replaces the GAN-based decoder with a conditional causal diffusion model. The encoder compresses spatio-temporal information into compact latents, while the decoder reconstructs videos through a reverse diffusion process conditioned on these latents. During inference, we incorporate a feature cache mechanism to generate videos of arbitrary length while maintaining temporal continuity and adopt sampling acceleration technique to enhance efficiency. Trained using only a basic MSE diffusion loss for reconstruction, along with KL term and LPIPS perceptual loss from scratch, extensive experiments demonstrate that CDT achieves state-of-the-art performance in video reconstruction tasks with just a single-step sampling. Even a scaled-down version of CDT (3$\times$ inference speedup) still performs comparably with top baselines. Moreover, the latent video generation model trained with CDT also exhibits superior performance. The source code and pretrained weights are available at https://github.com/ali-vilab/CDT.
Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need
Authors: Amal Shaheena, Nairouz Mrabahb, Riadh Ksantinia, Abdulla Alqaddoumia
Date: 2025-03-05 00:00:00
Tasks: All, Clustering, Deep Clustering

The recent advances in deep clustering have been made possible by significant progress in self-supervised and pseudo-supervised learning. However, the trade-off between self-supervision and pseudo-supervision can give rise to three primary issues. The joint training causes Feature Randomness and Feature Drift, whereas the independent training causes Feature Randomness and Feature Twist. In essence, using pseudo-labels generates random and unreliable features. The combination of pseudo-supervision and self-supervision drifts the reliable clustering-oriented features. Moreover, moving from self-supervision to pseudo-supervision can twist the curved latent manifolds. This paper addresses the limitations of existing deep clustering paradigms concerning Feature Randomness, Feature Drift, and Feature Twist. We propose a new paradigm with a new strategy that replaces pseudo-supervision with a second round of self-supervision training. The new strategy makes the transition between instance-level self-supervision and neighborhood-level self-supervision smoother and less abrupt. Moreover, it prevents the drifting effect that is caused by the strong competition between instance-level self-supervision and clustering-level pseudo-supervision. Moreover, the absence of the pseudo-supervision prevents the risk of generating random features. With this novel approach, our paper introduces a Rethinking of the Deep Clustering Paradigms, denoted by R-DC. Our model is specifically designed to address three primary challenges encountered in Deep Clustering: Feature Randomness, Feature Drift, and Feature Twist. Experimental results conducted on six datasets have shown that the two-level self-supervision training yields substantial improvements.
Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content
Authors: ZiCheng Zhang, Tengchuan Kou, Shushi Wang, Chunyi Li, Wei Sun, Wei Wang, Xiaoyu Li, ZongYu Wang, Xuezhi Cao, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1

Evaluating text-to-vision content hinges on two crucial aspects: visual quality and alignment. While significant progress has been made in developing objective models to assess these dimensions, the performance of such models heavily relies on the scale and quality of human annotations. According to Scaling Law, increasing the number of human-labeled instances follows a predictable pattern that enhances the performance of evaluation models. Therefore, we introduce a comprehensive dataset designed to Evaluate Visual quality and Alignment Level for text-to-vision content (Q-EVAL-100K), featuring the largest collection of human-labeled Mean Opinion Scores (MOS) for the mentioned two aspects. The Q-EVAL-100K dataset encompasses both text-to-image and text-to-video models, with 960K human annotations specifically focused on visual quality and alignment for 100K instances (60K images and 40K videos). Leveraging this dataset with context prompt, we propose Q-Eval-Score, a unified model capable of evaluating both visual quality and alignment with special improvements for handling long-text prompt alignment. Experimental results indicate that the proposed Q-Eval-Score achieves superior performance on both visual quality and alignment, with strong generalization capabilities across other benchmarks. These findings highlight the significant value of the Q-EVAL-100K dataset. Data and codes will be available at https://github.com/zzc-1998/Q-Eval.
BHViT: Binarized Hybrid Vision Transformer
Authors: Tian Gao, Zhiyuan Zhang, Yu Zhang, Huajun Liu, Kaijie Yin, Chengzhong Xu, Hui Kong
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Binarization, Quantization

Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision Transformers (ViTs) on edge devices. However, due to the structural differences between CNN and Transformer architectures, simply applying binary CNN strategies to the ViT models will lead to a significant performance drop. To tackle this challenge, we propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Initially, BHViT utilizes the local information interaction and hierarchical feature aggregation technique from coarse to fine levels to address redundant computations stemming from excessive tokens. Then, a novel module based on shift operations is proposed to enhance the performance of the binary Multilayer Perceptron (MLP) module without significantly increasing computational overhead. In addition, an innovative attention matrix binarization method based on quantization decomposition is proposed to evaluate the token's importance in the binarized attention matrix. Finally, we propose a regularization loss to address the inadequate optimization caused by the incompatibility between the weight oscillation in the binary layers and the Adam Optimizer. Extensive experimental results demonstrate that our proposed algorithm achieves SOTA performance among binary ViT methods.
CMMLoc: Advancing Text-to-PointCloud Localization with Cauchy-Mixture-Model Based Framework
Authors: Yanlong Xu, Haoxuan Qu, Jun Liu, Wenxiao Zhang, Xun Yang
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1

The goal of point cloud localization based on linguistic description is to identify a 3D position using textual description in large urban environments, which has potential applications in various fields, such as determining the location for vehicle pickup or goods delivery. Ideally, for a textual description and its corresponding 3D location, the objects around the 3D location should be fully described in the text description. However, in practical scenarios, e.g., vehicle pickup, passengers usually describe only the part of the most significant and nearby surroundings instead of the entire environment. In response to this $\textbf{partially relevant}$ challenge, we propose $\textbf{CMMLoc}$, an uncertainty-aware $\textbf{C}$auchy-$\textbf{M}$ixture-$\textbf{M}$odel ($\textbf{CMM}$) based framework for text-to-point-cloud $\textbf{Loc}$alization. To model the uncertain semantic relations between text and point cloud, we integrate CMM constraints as a prior during the interaction between the two modalities. We further design a spatial consolidation scheme to enable adaptive aggregation of different 3D objects with varying receptive fields. To achieve precise localization, we propose a cardinal direction integration module alongside a modality pre-alignment strategy, helping capture the spatial relationships among objects and bringing the 3D objects closer to the text modality. Comprehensive experiments validate that CMMLoc outperforms existing methods, achieving state-of-the-art results on the KITTI360Pose dataset. Codes are available in this GitHub repository https://github.com/kevin301342/CMMLoc.
STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks
Authors: Tianqing Zhang, Kairong Yu, Xian Zhong, Hongwei Wang, Qi Xu, Qiang Zhang
Date: 2025-03-04 00:00:00
Proceeding: CVPR 2025 1

Spiking Neural Networks (SNNs) have gained significant attention due to their biological plausibility and energy efficiency, making them promising alternatives to Artificial Neural Networks (ANNs). However, the performance gap between SNNs and ANNs remains a substantial challenge hindering the widespread adoption of SNNs. In this paper, we propose a Spatial-Temporal Attention Aggregator SNN (STAA-SNN) framework, which dynamically focuses on and captures both spatial and temporal dependencies. First, we introduce a spike-driven self-attention mechanism specifically designed for SNNs. Additionally, we pioneeringly incorporate position encoding to integrate latent temporal relationships into the incoming features. For spatial-temporal information aggregation, we employ step attention to selectively amplify relevant features at different steps. Finally, we implement a time-step random dropout strategy to avoid local optima. As a result, STAA-SNN effectively captures both spatial and temporal dependencies, enabling the model to analyze complex patterns and make accurate predictions. The framework demonstrates exceptional performance across diverse datasets and exhibits strong generalization capabilities. Notably, STAA-SNN achieves state-of-the-art results on neuromorphic datasets CIFAR10-DVS, with remarkable performances of 97.14%, 82.05% and 70.40% on the static datasets CIFAR-10, CIFAR-100 and ImageNet, respectively. Furthermore, our model exhibits improved performance ranging from 0.33\% to 2.80\% with fewer time steps. The code for the model is available on GitHub.
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment
Authors: Matthew DosSantos DiSorbo, Harang Ju, Sinan Aral
Date: 2025-03-04 00:00:00
Tasks: Decision Making, Transfer Learning

Large language models (LLMs), initially developed for generative AI, are now evolving into agentic AI systems, which make decisions in complex, real-world contexts. Unfortunately, while their generative capabilities are well-documented, their decision-making processes remain poorly understood. This is particularly evident when models are handling exceptions, a critical and challenging aspect of decision-making made relevant by the inherent incompleteness of contracts. Here we demonstrate that LLMs, even ones that excel at reasoning, deviate significantly from human judgments because they adhere strictly to policies, even when such adherence is impractical, suboptimal, or even counterproductive. We then evaluate three approaches to tuning AI agents to handle exceptions: ethical framework prompting, chain-of-thought reasoning, and supervised fine-tuning. We find that while ethical framework prompting fails and chain-of-thought prompting provides only slight improvements, supervised fine-tuning, specifically with human explanations, yields markedly better results. Surprisingly, in our experiments, supervised fine-tuning even enabled models to generalize human-like decision-making to novel scenarios, demonstrating transfer learning of human-aligned decision-making across contexts. Furthermore, fine-tuning with explanations, not just labels, was critical for alignment, suggesting that aligning LLMs with human judgment requires explicit training on how decisions are made, not just which decisions are made. These findings highlight the need to address LLMs' shortcomings in handling exceptions in order to guide the development of agentic AI toward models that can effectively align with human judgment and simultaneously adapt to novel contexts.
Learning to Reduce Search Space for Generalizable Neural Routing Solver
Authors: Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang
Date: 2025-03-05 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

Constructive neural combinatorial optimization (NCO) has attracted growing research attention due to its ability to solve complex routing problems without relying on handcrafted rules. However, existing NCO methods face significant challenges in generalizing to large-scale problems due to high computational complexity and inefficient capture of structural patterns. To address this issue, we propose a novel learning-based search space reduction method that adaptively selects a small set of promising candidate nodes at each step of the constructive NCO process. Unlike traditional methods that rely on fixed heuristics, our selection model dynamically prioritizes nodes based on learned patterns, significantly reducing the search space while maintaining solution quality. Experimental results demonstrate that our method, trained solely on 100-node instances from uniform distribution, generalizes remarkably well to large-scale Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) instances with up to 1 million nodes from the uniform distribution and over 80K nodes from other distributions.
COSINT-Agent: A Knowledge-Driven Multimodal Agent for Chinese Open Source Intelligence
Authors: Wentao Li, Congcong Wang, Xiaoxiao Cui, Zhi Liu, Wei Guo, Lizhen Cui
Date: 2025-03-05 00:00:00
Tasks: Multimodal Reasoning

Open Source Intelligence (OSINT) requires the integration and reasoning of diverse multimodal data, presenting significant challenges in deriving actionable insights. Traditional approaches, including multimodal large language models (MLLMs), often struggle to infer complex contextual relationships or deliver comprehensive intelligence from unstructured data sources. In this paper, we introduce COSINT-Agent, a knowledge-driven multimodal agent tailored to address the challenges of OSINT in the Chinese domain. COSINT-Agent seamlessly integrates the perceptual capabilities of fine-tuned MLLMs with the structured reasoning power of the Entity-Event-Scene Knowledge Graph (EES-KG). Central to COSINT-Agent is the innovative EES-Match framework, which bridges COSINT-MLLM and EES-KG, enabling systematic extraction, reasoning, and contextualization of multimodal insights. This integration facilitates precise entity recognition, event interpretation, and context retrieval, effectively transforming raw multimodal data into actionable intelligence. Extensive experiments validate the superior performance of COSINT-Agent across core OSINT tasks, including entity recognition, EES generation, and context matching. These results underscore its potential as a robust and scalable solution for advancing automated multimodal reasoning and enhancing the effectiveness of OSINT methodologies.
Leveraging Large Language Models to Develop Heuristics for Emerging Optimization Problems
Authors: Thomas Bömer, Nico Koltermann, Max Disselnmeyer, Laura Dörr, Anne Meyer
Date: 2025-03-05 00:00:00
Tasks: Combinatorial Optimization, In-Context Learning, Traveling Salesman Problem

Combinatorial optimization problems often rely on heuristic algorithms to generate efficient solutions. However, the manual design of heuristics is resource-intensive and constrained by the designer's expertise. Recent advances in artificial intelligence, particularly large language models (LLMs), have demonstrated the potential to automate heuristic generation through evolutionary frameworks. Recent works focus only on well-known combinatorial optimization problems like the traveling salesman problem and online bin packing problem when designing constructive heuristics. This study investigates whether LLMs can effectively generate heuristics for niche, not yet broadly researched optimization problems, using the unit-load pre-marshalling problem as an example case. We propose the Contextual Evolution of Heuristics (CEoH) framework, an extension of the Evolution of Heuristics (EoH) framework, which incorporates problem-specific descriptions to enhance in-context learning during heuristic generation. Through computational experiments, we evaluate CEoH and EoH and compare the results. Results indicate that CEoH enables smaller LLMs to generate high-quality heuristics more consistently and even outperform larger models. Larger models demonstrate robust performance with or without contextualized prompts. The generated heuristics exhibit scalability to diverse instance configurations.
Unified Mind Model: Reimagining Autonomous Agents in the LLM Era
Authors: Pengbo Hu, Xiang Ying
Date: 2025-03-05 00:00:00
Tasks: Instruction Following

Large language models (LLMs) have recently demonstrated remarkable capabilities across domains, tasks, and languages (e.g., ChatGPT and GPT-4), reviving the research of general autonomous agents with human-like cognitive abilities.Such human-level agents require semantic comprehension and instruction-following capabilities, which exactly fall into the strengths of LLMs.Although there have been several initial attempts to build human-level agents based on LLMs, the theoretical foundation remains a challenging open problem. In this paper, we propose a novel theoretical cognitive architecture, the Unified Mind Model (UMM), which offers guidance to facilitate the rapid creation of autonomous agents with human-level cognitive abilities. Specifically, our UMM starts with the global workspace theory and further leverage LLMs to enable the agent with various cognitive abilities, such as multi-modal perception, planning, reasoning, tool use, learning, memory, reflection and motivation. Building upon UMM, we then develop an agent-building engine, MindOS, which allows users to quickly create domain-/task-specific autonomous agents without any programming effort.