Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces com- putational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks ad- dress this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized processing. The high-frequency branch preserves full-resolution structural details, while the low-frequency branch processes downsampled inputs through dual sub-branches capturing short- and long-range dependencies. A Hybrid-Frequency Fusion mod- ule integrates these observations, guided by two novel objectives: Cross-Frequency Alignment Loss ensures semantic consistency between frequency components, and Cross-Frequency Balance Loss regulates gradient magnitudes across branches to stabilize training. Evaluated on DeepGlobe and Inria Aerial benchmarks, F2Net achieves state-of-the-art performance with mIoU of 80.22 and 83.39, respectively.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Sign Language Translation (SLT) converts continuous sign videos into spoken language text, yet current models, whether gloss-based or gloss-free, struggle with long or discourse-level inputs. Recent architectures such as TwoStreamNetwork and CV-SLT have nearly saturated short-sentence accuracy, but their performance degrades on long sentences and multi-sentence paragraphs. In real scenarios such as news, interviews or daily conversations, signers naturally produce extended signing sequences with complex contextual dependencies. Moreover, identifying precise gloss boundaries remains a key obstacle, while gloss-based methods, though often superior, incur heavy annotation costs. The community therefore needs a solution that mitigates gloss dependency while preserving translation quality. We present BoostSLT, a context-aware framework for enhancing semantic consistency over long sign sequences without gloss supervision. Instead of requiring explicit gloss segmentation, BoostSLT introduces an Energy-Aware Temporal Segmentation (EAT-Seg) module that dynamically partitions videos into semantically coherent fragments, followed by a Diffusion-based Semantic Reconstruction (DSR) module that stitches and refines fragment-level translations into globally fluent paragraphs. The framework is plug-and-play and model-agnostic, seamlessly integrating with existing gloss-based or gloss-free pipelines across languages. Experiments on PHOENIX-2014T, CSL-Daily, and Auslan-Daily show consistent BLEU and ROUGE-L gains, confirming that diffusion-driven semantic reconstruction effectively bridges local accuracy and global coherence in long-form SLT. Code is available at github.com/K1sna/BoostSLT.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Zhang, Mu, Ma, Tianren, Liu, Yunfan, Hu, Kun, Ye, Qixiang
Discrete Diffusion Models (DDMs) have shown great potential in image generation, especially when equipped with reinforcement learning (RL) techniques.However, a fundamental yet overlooked limitation is revealed in our experiments: severe imbalance of credit assignment across timesteps during training. As a result, early generation timesteps, which carry higher exploration potential and determine the global structure, provide a smaller contribution to policy optimization.To conquer this, we propose a simple-yet-effective approach, to Re-balance timestep credit of Reinforcement Learning (RebRL) for better exploration-exploitation trade-off and more efficient training of DDMs.RebRL is plug-and-play\textemdash simply replacing uniform temporal policy with strategic rebalancing along masking stages. RebRL is analytically plausible\textemdashderivation and analysis show that it enjoys a uniform token-level policy gradient, which benefits policy optimization.Experiments on text-to-image generation benchmarks show that RebRL achieves state-of-the-art performance on GenEval and improves human preference score by up to 3.40 while effectively reducing training steps by ~40\%.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Hallucination limits the reliability of multimodal large language models (MLLMs), and it is particularly damaging in video where errors manifest as distorted narratives rather than single-frame mistakes. We introduce a frame-first study of **Chimera Hallucination**: model stitches visual segments that exist in space and time but do not belong to the same event chain, producing a spurious continuous story. We introduce **CH-Risk**, a single-forward, reference-free risk estimate tailored to this failure mode. CH-Risk combines two complementary signals: SegCoverage@\alpha (\mathrm SCR @\alpha) measures how many event segments are needed to cover most text-to-frame support, exposing long-range stitching; Alignment with Early Temporal Pathway (AETP) measures rank consistency between support and the temporal pathway formed in early-middle layers, exposing stage mismatch. To turn risk into correction, we further propose **CH-M(itigation)**, a train-free two-stage intervention. Segment-aligned Stage-Aligned Frame Routing (sSAFR) re-weights frames before the mid-layer softmax to route attention toward a small set of pathway-aligned segments. Residual Token Calibration (RTC) then stabilizes token usage within selected segments. Extensive experiments across 9 benchmarks and 6 VideoLLMs show that CH-Risk can predict Chimera and that CH-M consistently reduces hallucination and improves task accuracy with negligible overhead (sub-5% latency, sub-2.5% memory, \~1% FLOPs).
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Luo, Yu, Zhu, Xiaogang, Zeng, Shan, Xiang, Wei, Bishop, Thomas Francis, Wang, Zhiyong, Hu, Kun
Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for single-crop prediction, they often struggle to generalize across diverse crop types, without addressing the unique crop phenological responses that are dynamically modulated by complex weather patterns. In this paper, we propose PhenoYieldNet, a multi-crop yield prediction framework that learns crop-specific phenology by explicitly modeling their responses with temporal drivers. Specifically, we develop a crop-aware temporal decoder consisting of a Crop Phenology Bank (CPB) and a Crop Phenology Attention (CPA) module. The CPB integrates a set of learnable embeddings, which leverage a query to guide the CPA module to learn the most relevant phenology patterns for the specific crop. And the CPA module explicitly captures multi-scale trend and variation components to construct temporal contexts, enabling the model to dynamically adjust the attention across different phenological stages. To learn robust and generalizable features for multi-crop prediction, the encoder is initialized with a pre-trained foundation model, and further adapted via a self-supervised Temporal Contrastive Adaptation strategy to align with agricultural temporal dynamics. Extensive experiments conducted on multi-crop datasets indicate that our proposed method significantly outperforms state-of-the-art methods, exhibiting strong generalization capabilities across different regions and crops.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表